{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "393ea781-035a-4ac1-9e78-7be8150c5dc4",
   "metadata": {},
   "source": [
    "# Best Fit (An example in applying the systematics)\n",
    "\n",
    "This notebook gives some examples on how to apply systematic effects (nusiance parameters) to the MC, using the given splines and gradients. We will reproduce the MC with best-fit nuisance parameters that closely resemble the data.\n",
    "\n",
    "Systematics are presented in two ways: \n",
    "1. bin-wise arrays of the quantity $1+(\\Delta N / N)$, where $\\Delta N$ is the difference in bin count given a $1\\sigma$ variation in the systematic parameter, and where $N$ is the nominal bin count, as a function of each bin.\n",
    "    - Exception: `hole_ice_scale` is not the 1-sigma variation but the change in parameter value from the nominal (-1) to a value of 0\n",
    "    - Exception: `domeff` is not the 1-sigma variation but the change in parameter value from the nominal (0.97) to a value of 0.98\n",
    "    - these are located at `/systematics/gradients`\n",
    "2. spline files (`.fits`) that return 3-d spline-interpolated values of a representative quantity such as $\\log_{10}(N)$ (where $N$ is the rate), as a function of $\\log_{10}(\\text{energy})$, $\\cos(\\text{zenith})$, and the value of the systematic. The exact output quantity may be different depending on each systematic. The methods to correctly read each spline file is included in this example. These are usually separated into splines that apply to only conventional or prompt atmospheric neutrino fluxes. To get the full effect of the systematic, the effects must be added after they are applied to the monte carlo.\n",
    "    - Exception: `AtmosphericKaonLossesSplines` (`kaon_scale`) and `AtmosphericZenithVariationSplines` (`airs_scale`) splines tabulate the change in systematic at the $1\\sigma$ variation in the nuisance parameter\n",
    "    - these are located at `/systematics/splines`\n",
    "    \n",
    "Due to the analysis procedure, different systematics are applied in different formats. Hence not every systematic is availale as a spline.\n",
    "\n",
    "To apply the systematics, we either apply a multiplicative factor on their weights (the case with `splines`) or the binned histogram bin counts (the case with `gradients`). \n",
    "\n",
    "**In this notebook, we will demonstrate how to apply the systematics.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c3c193-0181-447b-a920-b68fb1214ce0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a41d4fb-699c-431a-b3b2-5843b2de3aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "from scipy.interpolate import interp2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0e642d9-75e8-46fc-a062-1abd8b68dbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import photospline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3400544b-0f6c-4c62-b4e6-49e7bf790854",
   "metadata": {},
   "source": [
    "## Binning configuration\n",
    "\n",
    "Set the binning that we will use throughout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1af08703-a382-46bc-aeea-d19408d9c49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin edges for the energy binning\n",
    "energy_binedges = [499.99999999999994,\n",
    "                    629.4611936411585,\n",
    "                    792.4427886003042,\n",
    "                    997.6239672093516,\n",
    "                    1255.9311464092532,\n",
    "                    1581.1198370997542,\n",
    "                    1990.5071599010512,\n",
    "                    2505.8940256451765,\n",
    "                    3154.726089041722,\n",
    "                    3971.5552992382127,\n",
    "                    4999.879878540708,\n",
    "                    6294.46071281729,\n",
    "                    7924.237507234708,\n",
    "                    9975.999999999996]\n",
    "\n",
    "# bin edges for the cos(Zenith) binning\n",
    "zenith_binedges   = [-1.0,\n",
    "                    -0.95,\n",
    "                    -0.9,\n",
    "                    -0.85,\n",
    "                    -0.8,\n",
    "                    -0.75,\n",
    "                    -0.7,\n",
    "                    -0.6499999999999999,\n",
    "                    -0.6,\n",
    "                    -0.55,\n",
    "                    -0.5,\n",
    "                    -0.44999999999999996,\n",
    "                    -0.3999999999999999,\n",
    "                    -0.35,\n",
    "                    -0.29999999999999993,\n",
    "                    -0.25,\n",
    "                    -0.19999999999999996,\n",
    "                    -0.1499999999999999,\n",
    "                    -0.09999999999999998,\n",
    "                    -0.04999999999999993,\n",
    "                    0.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23f8559-7269-4fee-a011-898c112d3711",
   "metadata": {},
   "source": [
    "## Helper functions\n",
    "\n",
    "Handles the reading of all the splines and gradients. Usage examples are featured later on in the code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d72b7ea-30b3-4bf6-9d74-cb04ff94a439",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for reading AttenuationSplines\n",
    "# input is the true neutrino energy and coszenith\n",
    "def get_attenuation(ene,coszen,pdg,scale_nu,scale_nubar,filename):\n",
    "    Spline_nu    = photospline.SplineTable(filename+'numu.fits')\n",
    "    Spline_nubar = photospline.SplineTable(filename+'numubar.fits')\n",
    "    shift = np.zeros(len(pdg))\n",
    "    for i in range(len(pdg)) :\n",
    "        if i%10000 == 0: print('reached event '+str(i),end='\\r')\n",
    "        if pdg[i]>0 : shift[i] = Spline_nu.evaluate_simple([np.log10(ene[i]),coszen[i],scale_nu])        \n",
    "        else        : shift[i] = Spline_nubar.evaluate_simple([np.log10(ene[i]),coszen[i],scale_nubar])\n",
    "    return shift\n",
    "\n",
    "# for reading DOMEffSplines and HoleIceSplines\n",
    "# input is the reconstructed muon energy and coszenith\n",
    "def get_detector(ene,coszen,nominal,scale,filename):\n",
    "    Spline = photospline.SplineTable(filename)    \n",
    "    rate_nominal = Spline.evaluate_simple([np.log10(ene),coszen,nominal])\n",
    "    rate_scale   = Spline.evaluate_simple([np.log10(ene),coszen,scale])        \n",
    "    return np.power(10.,rate_scale-rate_nominal)\n",
    "\n",
    "# for reading AtmosphericKaonLossesSplines and AtmosphericZenithVariationSplines\n",
    "# input is the true neutrino energy and coszenith\n",
    "def get_sigma(var1,var2,scale,filename):\n",
    "    Spline = photospline.SplineTable(filename)    \n",
    "    one_sigma_shift = Spline.evaluate_simple([np.log10(var1),var2])        \n",
    "    return 1 + one_sigma_shift * scale\n",
    "\n",
    "# for reading all tables in the gradients/ directory\n",
    "def nsigma_variation_factor(gradient_filepath, sigma): \n",
    "    \n",
    "    data = None\n",
    "    with open(gradient_filepath, 'r') as file:\n",
    "    \n",
    "        print('opened file '+str(gradient_filepath))\n",
    "\n",
    "        data = json.load(file)\n",
    "    \n",
    "    # systematic_effect is an array of the quantity $1+(\\Delta N / N)$ for each bin \n",
    "    systematic_effect = np.asarray(data['effect'])\n",
    "    \n",
    "    # we apply an arbitrary sigma value assuming linear response ($(\\Delta N / N)$ is the 1 sigma variation)\n",
    "    systematic_effect = systematic_effect - 1\n",
    "    systematic_effect = systematic_effect * sigma\n",
    "    systematic_effect = systematic_effect + 1\n",
    "    \n",
    "    return systematic_effect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad50d47-1b23-4fad-8857-7cedd8c3bdd4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setup: Load the MC\n",
    "\n",
    "Since the splines are dependent on the flux component, the MC is loaded then multiplied by respective fluxes to obtain the prompt atmospheric, conventional atmospheric, and astrophysical components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf6afe1-afa9-422a-b246-261bb44e235a",
   "metadata": {},
   "source": [
    "Open the MC file and select the events within our energy and cosZenith binning range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fdf2c457-253d-46a2-b8c2-47faf6526790",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the data \n",
    "combined_MC = np.loadtxt('../monte_carlo.dat')\n",
    "\n",
    "# get the ranges that are within our binning\n",
    "combined_MC = combined_MC[combined_MC[:, 1] > energy_binedges[0]]\n",
    "combined_MC = combined_MC[combined_MC[:, 1] < energy_binedges[-1]]\n",
    "\n",
    "combined_MC = combined_MC[combined_MC[:, 2] > zenith_binedges[0]]\n",
    "combined_MC = combined_MC[combined_MC[:, 2] < zenith_binedges[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25dca87-03a2-4b4f-822b-748fe9f517f8",
   "metadata": {},
   "source": [
    "Separate the MC into Nu and NuBar events, since the flux for each is distinct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79b3b736-9e78-4379-911b-082057c802d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_MC_NuMu = combined_MC[combined_MC[:, 0] == 14]\n",
    "combined_MC_NuMuBar = combined_MC[combined_MC[:, 0] == -14]\n",
    "combined_MC_chargeSorted = np.concatenate((combined_MC_NuMu,combined_MC_NuMuBar))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068b290b-f110-436e-a403-f0e563efb1f7",
   "metadata": {},
   "source": [
    "Interpolate conventional, prompt, and astro fluxes from the tables provided in `/fluxes`. They can also be generated with a custom flux file or from NuFlux. This step takes a while, as we need to interpolate many times. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5569d0a3-692f-4dca-8d65-8e2921f82ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conventional atmospheric flux\n",
    "fluxfile = np.load('../fluxes/AtmMuFinalFluxatmospheric.npz')\n",
    "\n",
    "# Access the individual arrays\n",
    "cth_nodes = fluxfile['cth_nodes']\n",
    "energy_nodes = fluxfile['energy_nodes']\n",
    "atmflux = fluxfile['flux']\n",
    "\n",
    "atm_numu_flux = interp2d(np.log10(energy_nodes), cth_nodes, atmflux[0], kind='linear')\n",
    "atm_numubar_flux = interp2d(np.log10(energy_nodes), cth_nodes, atmflux[1], kind='linear')\n",
    "\n",
    "def atm_numu_flux_interp(energy, cth): \n",
    "    return atm_numu_flux(np.log10(energy), cth)\n",
    "def atm_numubar_flux_interp(energy, cth):\n",
    "    return atm_numubar_flux(np.log10(energy), cth)\n",
    "\n",
    "atm_numu_flux_interp = np.vectorize(atm_numu_flux_interp)\n",
    "atm_numubar_flux_interp = np.vectorize(atm_numubar_flux_interp)\n",
    "\n",
    "# do the interpolation\n",
    "NuMu_flux_conv = atm_numu_flux_interp(combined_MC_NuMu[:,3],combined_MC_NuMu[:,4])\n",
    "NuMuBar_flux_conv = atm_numubar_flux_interp(combined_MC_NuMuBar[:,3],combined_MC_NuMuBar[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6777473-e195-4956-ba58-71108039eacc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prompt atmospheric flux\n",
    "fluxfile = np.load('../fluxes/AtmMuFinalFluxprompt_atmospheric.npz')\n",
    "\n",
    "# Access the individual arrays\n",
    "cth_nodes = fluxfile['cth_nodes']\n",
    "energy_nodes = fluxfile['energy_nodes']\n",
    "atmflux = fluxfile['flux']\n",
    "\n",
    "atm_numu_flux = interp2d(np.log10(energy_nodes), cth_nodes, atmflux[0], kind='linear')\n",
    "atm_numubar_flux = interp2d(np.log10(energy_nodes), cth_nodes, atmflux[1], kind='linear')\n",
    "\n",
    "def atm_numu_flux_interp(energy, cth): \n",
    "    return atm_numu_flux(np.log10(energy), cth)\n",
    "def atm_numubar_flux_interp(energy, cth):\n",
    "    return atm_numubar_flux(np.log10(energy), cth)\n",
    "\n",
    "atm_numu_flux_interp = np.vectorize(atm_numu_flux_interp)\n",
    "atm_numubar_flux_interp = np.vectorize(atm_numubar_flux_interp)\n",
    "\n",
    "# do the interpolation\n",
    "NuMu_flux_pr = atm_numu_flux_interp(combined_MC_NuMu[:,3],combined_MC_NuMu[:,4])\n",
    "NuMuBar_flux_pr = atm_numubar_flux_interp(combined_MC_NuMuBar[:,3],combined_MC_NuMuBar[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d6c7b8-63e2-4bce-98a6-039ff273aac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# astrophysical flux\n",
    "fluxfile = np.load('../fluxes/AstroMuFinalFlux.npz')\n",
    "\n",
    "# Access the individual arrays\n",
    "cth_nodes = fluxfile['cth_nodes']\n",
    "energy_nodes = fluxfile['energy_nodes']\n",
    "astroflux = fluxfile['flux']\n",
    "\n",
    "astro_numu_flux = interp2d(np.log10(energy_nodes), cth_nodes, astroflux[0], kind='linear')\n",
    "astro_numubar_flux = interp2d(np.log10(energy_nodes), cth_nodes, astroflux[1], kind='linear')\n",
    "\n",
    "def astro_numu_flux_interp(energy, cth): \n",
    "    return astro_numu_flux(np.log10(energy), cth)\n",
    "def astro_numubar_flux_interp(energy, cth):\n",
    "    return astro_numubar_flux(np.log10(energy), cth)\n",
    "\n",
    "astro_numu_flux_interp = np.vectorize(astro_numu_flux_interp)\n",
    "astro_numubar_flux_interp = np.vectorize(astro_numubar_flux_interp)\n",
    "\n",
    "# do the interpolation\n",
    "NuMu_flux_astro = astro_numu_flux_interp(combined_MC_NuMu[:,3],combined_MC_NuMu[:,4])\n",
    "NuMuBar_flux_astro = astro_numubar_flux_interp(combined_MC_NuMuBar[:,3],combined_MC_NuMuBar[:,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71443375-34b4-4bfc-b88d-fd93c15f0cf4",
   "metadata": {},
   "source": [
    "Consolidate the weights into the prompt, conventional, and astro parts. The physical weight is the `oneweight` (provided in the MC file) multipled with the relevant flux, and multiplied by the livetime of 7.6 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba32c7fb-67bb-4446-8091-f8357ca88f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the oneweight multiplied by the flux and 7.6 years livetime in seconds\n",
    "NuMu_totalWeight_conv = combined_MC_NuMu[:,5] * NuMu_flux_conv  * 239673600 \n",
    "NuMuBar_totalWeight_conv = combined_MC_NuMuBar[:,5] * NuMuBar_flux_conv  * 239673600\n",
    "\n",
    "totalWeights_conv = np.concatenate((NuMu_totalWeight_conv,NuMuBar_totalWeight_conv))\n",
    "\n",
    "NuMu_totalWeight_pr = combined_MC_NuMu[:,5] * NuMu_flux_pr  * 239673600 \n",
    "NuMuBar_totalWeight_pr = combined_MC_NuMuBar[:,5] * NuMuBar_flux_pr  * 239673600\n",
    "\n",
    "totalWeights_pr= np.concatenate((NuMu_totalWeight_pr,NuMuBar_totalWeight_pr))\n",
    "\n",
    "NuMu_totalWeight_astro = combined_MC_NuMu[:,5] * NuMu_flux_astro  * 239673600 \n",
    "NuMuBar_totalWeight_astro = combined_MC_NuMuBar[:,5] * NuMuBar_flux_astro  * 239673600\n",
    "\n",
    "totalWeights_astro= np.concatenate((NuMu_totalWeight_astro,NuMuBar_totalWeight_astro))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121e2066-a6fc-45f1-b059-b9c4d440d118",
   "metadata": {},
   "source": [
    "Bin the nominal (or unfitted) MC into histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ca51477-81ce-4669-90c1-9856c4540967",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = combined_MC_chargeSorted[:,2] # cosZenith\n",
    "y_data = combined_MC_chargeSorted[:,1] # energy\n",
    "\n",
    "x_bin_edges = zenith_binedges\n",
    "y_bin_edges = energy_binedges\n",
    "\n",
    "hist_MC_conv_nominal, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_conv, bins=[x_bin_edges, y_bin_edges])\n",
    "hist_MC_pr_nominal, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_pr, bins=[x_bin_edges, y_bin_edges])\n",
    "hist_MC_astro_nominal, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_astro, bins=[x_bin_edges, y_bin_edges])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b40e31-8db9-4d5d-8165-59e96873f4ee",
   "metadata": {},
   "source": [
    "At this point, our MC information is contained in the array `combined_MC_chargeSorted`, with scaled weight arrays `totalWeights_conv`, `totalWeights_pr`, and `totalWeights_astro` for the weights, depending on the flux."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9caa90-07b7-4fbd-b32b-b10217aec023",
   "metadata": {},
   "source": [
    "## Apply the nusiance parameters that are spline files\n",
    "\n",
    "Spline files are available for following nusiance parameters\n",
    "- `airs_scale` (`AtmosphericZenithVariationSplines`)\n",
    "- `kaon_scale` (`AtmosphericKaonLossesSplines`) \n",
    "- `nu_scale`/`nubar_scale` (`AttenuationSplines`) \n",
    "- `domeff` (`DOMEffSplines`)\n",
    "- `hole_ice_scale` (`HoleIceSplines`).\n",
    "\n",
    "The functions correspond to their corresponding splines in the following way: \n",
    "- `AttenuationSplines` is handled by the function `get_attenuation`\n",
    "    - input is **true energy and cos(Zenith)**\n",
    "- `DOMEffSplines` and `HoleIceSplines` are handled by the function `get_detector`\n",
    "    - input is **reconstructed energy and cos(Zenith)**\n",
    "- `AtmosphericKaonLossesSplines` and `AtmosphericZenithVariationSplines` are handled by the function `get_sigma`\n",
    "    - input is **true energy and cos(Zenith)**\n",
    "    - the factor **only applies to the conventional atmospheric flux component**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46cc932c-6679-4f30-97d9-3fda13a5314b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reached event 22660000\r"
     ]
    }
   ],
   "source": [
    "attenuationFactor_conv = get_attenuation(combined_MC_chargeSorted[:,3], #true E\n",
    "                                         combined_MC_chargeSorted[:,4], #true cosZenith\n",
    "                                         combined_MC_chargeSorted[:,0],\n",
    "                                         1.00, 1.01, \n",
    "                                         '../systematics/splines/AttenuationSplines/STERILE_attenuation_spline_atmConv_')\n",
    "attenuationFactor_pr = get_attenuation(combined_MC_chargeSorted[:,3], #true E\n",
    "                                         combined_MC_chargeSorted[:,4], #true cosZenith\n",
    "                                         combined_MC_chargeSorted[:,0],\n",
    "                                         1.00, 1.01, \n",
    "                                         '../systematics/splines/AttenuationSplines/STERILE_attenuation_spline_atmPrompt_')\n",
    "attenuationFactor_astro = get_attenuation(combined_MC_chargeSorted[:,3], #true E\n",
    "                                         combined_MC_chargeSorted[:,4], #true cosZenith\n",
    "                                         combined_MC_chargeSorted[:,0],\n",
    "                                         1.00, 1.01, \n",
    "                                         '../systematics/splines/AttenuationSplines/STERILE_attenuation_spline_diffuseAstro_mu_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0817412-cc99-4103-84a6-b0adec8f9cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "holeiceFactor_conv = get_detector(combined_MC_chargeSorted[:,1],#reco E\n",
    "                                  combined_MC_chargeSorted[:,2],#reco cosZenith\n",
    "                                  -1.00,-3.32,\n",
    "                                  '../systematics/splines/HoleIceSplines/STERILE_holeice_spline_stacked_atmConv_track.fits')\n",
    "\n",
    "holeiceFactor_pr = get_detector(combined_MC_chargeSorted[:,1],\n",
    "                                  combined_MC_chargeSorted[:,2],\n",
    "                                  -1.00,-3.32,\n",
    "                                  '../systematics/splines/HoleIceSplines/STERILE_holeice_spline_stacked_atmPrompt_track.fits')\n",
    "\n",
    "holeiceFactor_astro = get_detector(combined_MC_chargeSorted[:,1],\n",
    "                                  combined_MC_chargeSorted[:,2],\n",
    "                                  -1.00,-3.32,\n",
    "                                  '../systematics/splines/HoleIceSplines/STERILE_holeice_spline_stacked_diffuseAstro_mu_track.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94a53c82-63ea-4aca-b4eb-76f37796bbe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "domeffFactor_conv = get_detector(combined_MC_chargeSorted[:,1],#reco E\n",
    "                                  combined_MC_chargeSorted[:,2],#reco cosZenith\n",
    "                                  1.27,1.26,\n",
    "                                  '../systematics/splines/DOMEffSplines/STERILE_domefficiency_spline_stacked_atmConv_track.fits')\n",
    "\n",
    "domeffFactor_pr = get_detector(combined_MC_chargeSorted[:,1],\n",
    "                                  combined_MC_chargeSorted[:,2],\n",
    "                                  1.27,1.26,\n",
    "                                  '../systematics/splines/DOMEffSplines/STERILE_domefficiency_spline_stacked_atmPrompt_track.fits')\n",
    "\n",
    "domeffFactor_astro = get_detector(combined_MC_chargeSorted[:,1],\n",
    "                                  combined_MC_chargeSorted[:,2],\n",
    "                                  1.27,1.26,\n",
    "                                  '../systematics/splines/DOMEffSplines/STERILE_domefficiency_spline_stacked_diffuseAstro_mu_track.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e17323e4-d621-4b00-b3af-2555ec9f0c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "atmkaonlossesFactor_conv = get_sigma(combined_MC_chargeSorted[:,3], #true E\n",
    "                                     combined_MC_chargeSorted[:,4], #true cosZenith\n",
    "                                     -0.16, '../systematics/splines/AtmosphericKaonLossesSplines/kaon_loses_1s.fits')\n",
    "\n",
    "atmzenithvariationFactor_conv = get_sigma(combined_MC_chargeSorted[:,3], #true E\n",
    "                                          combined_MC_chargeSorted[:,4], #true cosZenith\n",
    "                                          -0.10, '../systematics/splines/AtmosphericZenithVariationSplines/atm_density_1s.fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21499d1a-b9ed-4fab-b9f2-e08cea584100",
   "metadata": {},
   "source": [
    "Apply the splined factors to the respective weights. *Ignore kaon reinteraction for now.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91daa145-e855-4f6e-b0c1-280d9ceb599c",
   "metadata": {},
   "outputs": [],
   "source": [
    "totalWeights_conv = totalWeights_conv * attenuationFactor_conv * holeiceFactor_conv * domeffFactor_conv * atmzenithvariationFactor_conv \n",
    "totalWeights_pr = totalWeights_pr * attenuationFactor_pr * holeiceFactor_pr * domeffFactor_pr\n",
    "totalWeights_astro = totalWeights_astro * attenuationFactor_astro * holeiceFactor_astro * domeffFactor_astro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f9f539-276e-4778-8234-7deff9a1d0ea",
   "metadata": {},
   "source": [
    "## Apply the remaining nusiance parameters that are gradient files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a6c38f-a287-4e66-97ed-a2369437178e",
   "metadata": {},
   "source": [
    "The following are dictionaries that contain the filepaths of the systematics files, and the perturbation at the best fit point, given in terms of # of sigma (except for domeff and hole_ice_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "31b4e645-bd22-42fb-8303-5ffff7db99ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "systematics_files = {\n",
    "    'atmospheric_density': '../systematics/gradients/airs_scale_1.00.json',\n",
    "    'astro_spectral_shift': '../systematics/gradients/astro_delta_0.36.json',\n",
    "    'astro_norm': '../systematics/gradients/astro_norm_1.15.json',\n",
    "    'conventional_norm': '../systematics/gradients/conv_norm_1.40.json',\n",
    "    'conventional_spectral_shift': '../systematics/gradients/deltagamma_0.03.json',\n",
    "    'optical_module_eff': '../systematics/gradients/domeff_1.28.json',\n",
    "    'forward_hole_ice': '../systematics/gradients/hole_ice_scale_0.00.json',\n",
    "    'bulk_ice_grad_0': '../systematics/gradients/ice_grad_0_1.00.json',\n",
    "    'bulk_ice_grad_1': '../systematics/gradients/ice_grad_1_1.00.json',\n",
    "    'kaon_reinteraction': '../systematics/gradients/kaon_scale_1.00.json',\n",
    "    'cross_section_nu': '../systematics/gradients/nu_scale_1.03.json',\n",
    "    'cross_section_nubar': '../systematics/gradients/nubar_scale_1.07.json',\n",
    "    'WM': '../systematics/gradients/WM_0.40.json',\n",
    "    'WP': '../systematics/gradients/WP_0.40.json',\n",
    "    'YM': '../systematics/gradients/YM_0.30.json',\n",
    "    'YP': '../systematics/gradients/YP_0.30.json',\n",
    "    'ZM': '../systematics/gradients/ZM_0.12.json',\n",
    "    'ZP': '../systematics/gradients/ZP_0.12.json',\n",
    "}\n",
    "\n",
    "systematics_best_fit_sigma = {\n",
    "    'atmospheric_density': -0.1,\n",
    "    'astro_spectral_shift': +0.0555555, #need to flip sign of this due to golem/pisa implementation differences \n",
    "    'astro_norm': 0.13888888,\n",
    "    'conventional_norm': 0.25,\n",
    "    'conventional_spectral_shift': -2.33333333, #need to flip sign of this due to golem/pisa implementation differences \n",
    "    'optical_module_eff': -1,\n",
    "    'forward_hole_ice': -2.32,\n",
    "    'bulk_ice_grad_0': -0.08,\n",
    "    'bulk_ice_grad_1': 0.58,\n",
    "    'kaon_reinteraction': -0.16,\n",
    "    'cross_section_nu': 0.0,\n",
    "    'cross_section_nubar': 0.13333333,\n",
    "    'WM': -0.025,\n",
    "    'WP': 0.025,\n",
    "    'YM': -0.2,\n",
    "    'YP': -0.13333333,\n",
    "    'ZM': -0.08333333,\n",
    "    'ZP': -0.16666666,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9054b2e2-0602-48e8-a35a-097eae206949",
   "metadata": {},
   "source": [
    "Re-bin the monte carlo, with the spline-updated weights, so that we can then apply the gradient effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "021512f7-ac3e-42ac-bd95-ca05d4af81e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = combined_MC_chargeSorted[:,2] # cosZenith\n",
    "y_data = combined_MC_chargeSorted[:,1] # energy\n",
    "\n",
    "x_bin_edges = zenith_binedges \n",
    "y_bin_edges = energy_binedges\n",
    "\n",
    "hist_MC_conv, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_conv, bins=[x_bin_edges, y_bin_edges])\n",
    "hist_MC_pr, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_pr, bins=[x_bin_edges, y_bin_edges])\n",
    "hist_MC_astro, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_astro, bins=[x_bin_edges, y_bin_edges])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "911d91fe-2294-4727-a026-14233b786e4e",
   "metadata": {},
   "source": [
    "Apply the remaining nusiance parameter variations, given as a gradient for each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b9e0df9-3927-4594-aa19-22718cac84d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "opened file ../systematics/gradients/astro_delta_0.36.json\n",
      "opened file ../systematics/gradients/astro_norm_1.15.json\n",
      "opened file ../systematics/gradients/conv_norm_1.40.json\n",
      "opened file ../systematics/gradients/deltagamma_0.03.json\n",
      "opened file ../systematics/gradients/ice_grad_0_1.00.json\n",
      "opened file ../systematics/gradients/ice_grad_1_1.00.json\n",
      "opened file ../systematics/gradients/WM_0.40.json\n",
      "opened file ../systematics/gradients/WP_0.40.json\n",
      "opened file ../systematics/gradients/YM_0.30.json\n",
      "opened file ../systematics/gradients/YP_0.30.json\n",
      "opened file ../systematics/gradients/ZM_0.12.json\n",
      "opened file ../systematics/gradients/ZP_0.12.json\n"
     ]
    }
   ],
   "source": [
    "total_effect = nsigma_variation_factor(systematics_files['astro_spectral_shift'], systematics_best_fit_sigma['astro_spectral_shift'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['astro_norm'], systematics_best_fit_sigma['astro_norm'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['conventional_norm'], systematics_best_fit_sigma['conventional_norm'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['conventional_spectral_shift'], systematics_best_fit_sigma['conventional_spectral_shift'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['bulk_ice_grad_0'], systematics_best_fit_sigma['bulk_ice_grad_0'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['bulk_ice_grad_1'], systematics_best_fit_sigma['bulk_ice_grad_1'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['WM'], systematics_best_fit_sigma['WM'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['WP'], systematics_best_fit_sigma['WP'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['YM'], systematics_best_fit_sigma['YM'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['YP'], systematics_best_fit_sigma['YP'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['ZM'], systematics_best_fit_sigma['ZM'])\n",
    "total_effect *= nsigma_variation_factor(systematics_files['ZP'], systematics_best_fit_sigma['ZP'])\n",
    "\n",
    "hist_varied = (hist_MC_conv+hist_MC_astro+hist_MC_pr) * total_effect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20507da-ebbc-4bde-ad3f-1dd645d60be7",
   "metadata": {},
   "source": [
    "Open and bin the data to plot the data against the best-fit MC for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec71b6a5-6480-4d76-9a19-0a39288d4275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the data for comparison\n",
    "combined_data = np.loadtxt('../data.dat')\n",
    "\n",
    "# get the ranges that are within our binning\n",
    "combined_data = combined_data[combined_data[:, 0] > energy_binedges[0]]\n",
    "combined_data = combined_data[combined_data[:, 0] < energy_binedges[-1]]\n",
    "\n",
    "combined_data = combined_data[np.cos(combined_data[:, 1]) > zenith_binedges[0]]\n",
    "combined_data = combined_data[np.cos(combined_data[:, 1]) < zenith_binedges[-1]]\n",
    "\n",
    "x_data = np.cos(combined_data[:,1]) #cosZenith\n",
    "y_data = combined_data[:,0] # energy\n",
    "\n",
    "x_bin_edges = zenith_binedges\n",
    "y_bin_edges = energy_binedges\n",
    "\n",
    "hist_data, x_edges, y_edges = np.histogram2d(x_data, y_data, bins=[x_bin_edges, y_bin_edges])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62dcad69-0aa6-4aed-94c7-3af801dce9de",
   "metadata": {},
   "source": [
    "Plot a histogram that compares unfitted MC, best-fit MC, and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5ffcfe30-d8f6-4b7c-8dff-13457003867f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "hist_MC = hist_MC_conv_nominal+hist_MC_astro_nominal+hist_MC_pr_nominal\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=300)\n",
    "gs = gridspec.GridSpec(2, 2, height_ratios=[1, 0.4], hspace=0.2)\n",
    "\n",
    "# Create the main axes for the histogram plots\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "\n",
    "# Create the axes for the ratio plots\n",
    "ax1r = fig.add_subplot(gs[1, 0], sharex=ax1)\n",
    "ax2r = fig.add_subplot(gs[1, 1], sharex=ax2)\n",
    "\n",
    "ax1.stairs(np.sum(hist_MC,0), energy_binedges, edgecolor='blue', linestyle='--')\n",
    "ax1.stairs(np.sum(hist_varied,0), energy_binedges, edgecolor='cornflowerblue', linestyle='--')\n",
    "ax1.stairs(np.sum(hist_data,0), energy_binedges, edgecolor='red')\n",
    "ax1.set_xscale('log'); ax1.set_xlim(energy_binedges[0], energy_binedges[-1])\n",
    "ax1.set_ylabel('counts'); ax1.set_yscale('log')\n",
    "\n",
    "# Plot the histogram\n",
    "ax2.stairs(np.sum(hist_MC,1), zenith_binedges, edgecolor='blue', linestyle='--', label='MC (unfitted)')\n",
    "ax2.stairs(np.sum(hist_varied,1), zenith_binedges, edgecolor='cornflowerblue', linestyle='--', label=r'MC (best fit)')\n",
    "ax2.stairs(np.sum(hist_data,1), zenith_binedges, edgecolor='red', label=r'data')\n",
    "ax2.set_xlim(zenith_binedges[0], zenith_binedges[-1])\n",
    "\n",
    "ax2.legend(fontsize=8)\n",
    "\n",
    "# Plot the ratio plots\n",
    "ax1r.stairs(np.sum(hist_varied,0) / np.sum(hist_MC,0), energy_binedges, edgecolor='purple')\n",
    "ax2r.stairs(np.sum(hist_varied,1) / np.sum(hist_MC,1), zenith_binedges, edgecolor='purple')\n",
    "\n",
    "# Set up the ratio axes\n",
    "ax1r.set_xlabel('muon energy / GeV')\n",
    "ax1r.set_ylabel('BF/unfitted')\n",
    "ax1r.set_xscale('log'); ax1r.set_ylim(0.8,1.2)\n",
    "\n",
    "ax2r.set_xlabel('cos(Zenith)')\n",
    "ax2r.set_ylim(0.8,1.2)\n",
    "\n",
    "ax1r.axhline(y=1, color='gray', linestyle='--', linewidth=1)\n",
    "ax2r.axhline(y=1, color='gray', linestyle='--', linewidth=1)\n",
    "\n",
    "total_entries=int(np.sum(hist_MC))\n",
    "\n",
    "ax1.text(0.95, 0.95, f'data: {int(np.sum(hist_data))}\\n unfitted: {int(np.sum(hist_MC))}\\n best fit: {int(np.sum(hist_varied))}',\n",
    "       transform=ax1.transAxes, # Use axis coordinate system\n",
    "       horizontalalignment='right', # Align to the right edge\n",
    "       verticalalignment='top', # Align to the top edge\n",
    "       fontsize=8, # Font size\n",
    "       bbox=dict(facecolor='white', alpha=0.5))\n",
    "\n",
    "#plt.savefig('best_fit.png', bbox_inches='tight',dpi=400) # saves the current figure into a pdf page\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f6441d-6183-4bd8-970b-5fcf47c0bd4d",
   "metadata": {},
   "source": [
    "## More example plots to show various variations of a single nuisance parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db01edac-c7ec-4c02-8374-5964fea0298e",
   "metadata": {},
   "source": [
    "Example: `ice_grad_0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5e769345-3d31-435d-9c97-edc95a986592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "opened file ../systematics/gradients/ice_grad_0_1.00.json\n"
     ]
    },
    {
     "data": {
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aNLBIstx000355msP/y8vvyra9wzG9lQBbdq0sfnp2rWr0SkBAAAAAKqod955R2FhYZKkfv36KSgoKE+b5cuXa+fOnZKyFzkeM2aM3VhPPfWUdbu4awnkJysrSzNnzpQkNW7cWJ9++qk8PDzstnVxcclzB3XOVDH9+vXTI488Yvc4T09Pvffee5KyR5WUxutIS0vT4sWLJUm33nqr/vOf/+Rp4+rqqo8++kienp6FijlixIgCpz6qW7euatas6XC/u7u79f08efKkfvvtN5v9OeuVSNJNN92kZ555Jk+MatWqaeHChapWrVqh8i6sixcvWrcLM0LHx8dHUvbd7yXVq1cvPfjgg3mer169ukJDQyVlfxY//PDDPG1K+zNWmL+zkYqSX0ne15J+lktLenq6FixYIEm64YYb8kw5J2VPgzdv3jzVqVNHkqx/a0eeeOIJm3WEctSuXds6UuTAgQNKSkoqWfJAMTElFAAAAACgwnhtRZKSUh0vLFpZ1PB20YsDaxidRqnbunWrnn32WUlS/fr17c65L/3vgludOnXsXqDLccstt8jFxUVZWVmKiYkp9Xx/++03a9zRo0cXaaqpmJgY/fLLL5KkQYMG5ds2MDBQdevWVXx8vHbt2qW77767+ElL+uWXX6wXG4cNG2ZdaPdqDRo00D333KO1a9cWGHPIkCFFziM9PV1nz57VpUuXrAsCWywW6/79+/fbrBXxyy+/KCEhQZI0fPhwh3PoN2nSRN27d9f69euLnJMjaWlp1m13d/cC2+cUri5fvlzivh0t8C1JISEhatOmjSIiIrRx40abfWXxGSvO39mZipJfcd9Xe4r6WS4tv/zyixITEyVlF2scTfXk5+enQYMG6YMPPtCff/6p2NhY+fv7222b33uY+zUcP35ct9xyS7FzB4qLgkUVEBERYfM4OjpaAQEBBmUDAAAAAMWXlJqlxBRLwQ0rvLIvyji6iJ3byZMn8233ySefaMSIEYXqLyIiQv369ZPZbJaHh4eWL1+uBg0a5GkXFxenHTt2SJIeeuihfIsE1apVsxYsymKB2H379lm3O3bsWKRjw8PDrdsPPvig3Tu97YmLiytSP/b88ccf1u2CLqIGBQUVqmBx0003FarvlJQUzZkzR19++aUiIiJ05coVh23j4+NtHudelyI4ODjffkJCQkq1YJF7pEnuReEdSU9PlyR5eXmVuO/CvNaIiAgdOXJEGRkZ1oJKWXzGCvt3NkpR8ivu+5qjJJ/l0pL7v+Xbb78937a33367tQj8xx9/OCxYOFo3SJLNgt25Rx0BzkTBAgAAAABQYdTwdpEzLuYbLft1Vh7Hjx9X9+7dlZCQIFdXVy1dulSdOnWy23bbtm3W7e7du+cb96+//pLZbJakMrkxL/dFSEcX/xw5d+5csfpMTU0t1nG55YxSkLJHsuSnXr16hYpZq1atAtucOHFCXbt21fHjxwsV8+rRCUXJ216xqySqV69u3S7MNE8pKSmSCjd9VEEK+1otFosSEhKsj8viM1aYv7ORipJfcd9XqeSf5dJy4cIF63ZBn/mGDRvaPe5q3t7eDvflLvzmV6AByhIFCwAAAABAhVEZp0kySu672a+2du1avfjii2rUqJF++OEHh+2aNGlSYD9nzpzR3XffrTNnzshkMunjjz9Wv379HLbPfUfxzTffnG/svXv3WrcLupu6pAozIiW33Bf7vvjii0LfGV5eLxg7moomt6FDh+r48eMymUwaOXKkBg8erMDAQNWrV886hVJWVpY1Vu4pda5+XND7ffWxJeXp6WmdMik6OjrftgkJCdaCRWkUyor7WsviM1aYv7ORipJfST5DJf0slwVn/zcBGIWCBQAAAAAAVdCNN97ocF/OVDPVqlXLt11B4uPj1a1bNx07dkySNHfuXA0bNizfY06dOmXdLmhUQ86UQCaTSV27di12no7UrVvXun3mzBldf/31hT42ZwFcKTu/kryPRZX7gvS5c+fUqlUrh23Pnz9fKn1GRkZap/J67rnn9Prrr9ttl3sUxdVyT0dz9uzZfPMu7uiC/AQGBmr79u2KioqS2WyWm5v9y2aRkZE2x5TU2bNn8y185LxWk8lk87c18jNWGDl36+es+eDI/7d331FRXH8bwJ+ldymKXTABFHtXxIY9llijKNbYkmh++sYWNYomscUkapqxBWPFaEwiEmNFRQVB7C2CooIVG0qTsvf9g7OTRbYBy67A8zmHk5G5beYum5n5zr1XEfzRt8KeV318lvVF+W/iwYMHGv8mHj58qDIfUUlTusaYEhEREREREdEbITk5Gd26dcOVK1cAAEuWLMHEiRO15lN+uKlYJ0Bd+Vu2bAEAdOvWrcBTNumiSZMm0rbyVFW6aNy4sbS9f/9+vbVJF3Xr1pW2ldc5UEXbfl0pr5/p7+9fqPrq168vbSuPnlFF2/7CaNOmDYDcB+iKxaxVOXr0qLTt6+tb5Hp1PVZPT8886ywY4zNWkJFGimm2FItGq/Pvv/8WpUlqFfa86uOzDBR8VJYqykGoU6dOaUwbFRWlMh9RScOABRERERERERHpVVpaGnr27IkzZ84AAObMmYOZM2fqlFd53nlNDwQ//fRT6Q1nXcsuqIYNG0pvaK9bt06ntQ0UPDw8UKdOHQBAcHBwnpEjxa1Zs2YoVy53+rRNmzapnSrm4cOHGqf8KgjFWiKA5jUSfv75Z7X7mjZtKr3prqndd+/eLZYH9H379pW2g4KCVKaRy+XYuHEjAMDR0RF+fn5FrvfXX39Vu+/06dPSNGmdO3fOs88YnzHlxck1BRQBoGbNmgByF29WF5TIzMzE77//rr8GKinsedXHZxko2LlSp2nTpnB0dASQezzq1pV4+fIlfvvtNwBAnTp1iiWAS2QoDFgQERERERERkd5kZmaiX79+OHHiBABg8uTJ+PLLL3XOr3jLHQAWLVqk8qH1N998Iz0sHDFiBDp06FC0RqthYmKC6dOnAwASExMxYsQIZGZmqkwrl8tx7969PL/77LPPAAAZGRno37+/xumXXr16hZ9++gkZGRlFbreVlZU09daZM2fw7bffqmzvhAkT9FIfkPuWuoK6B8WrVq3Cn3/+qbYMS0tLjB49GgBw7tw5LFu2LF+a7OxsjBs3Tm0/FEWLFi3Qtm1bAMD69esRERGRL80333yDq1evAsj9bJubmxe53t27d0sPm5WlpKRg/PjxAHI/ixMmTMiXxtCfMeUH4Tdu3NCYtn379tL2N998k2+/EAKTJ0/O93ejL4U9r/r4LAMFO1fqWFpaYuzYsQByR34sWLAgXxohBCZNmoTHjx8DACZNmlSouojeFFzDgoiIiIiIiIj0ZsiQIdLb7x07dsSYMWPyLKT9OgsLizzzsvfq1Quenp6IjY3F/v378c477+Djjz9G5cqVcfPmTaxdu1Yqv02bNli9enWxHs/EiRMREhKCAwcO4I8//kD9+vXx0UcfoVmzZrCxscGDBw8QGRmJbdu2YejQoZg/f76Ud8iQIdi3bx9+/fVXxMTEoE6dOpgwYQLat2+PChUqIDU1FTdu3EB4eDh27dqFp0+fal3jQ1fz58/Hjh078ODBA0ybNg1nz57F8OHDUaFCBcTFxWHlypU4efIkWrRoIU0lU5QpbBo3box69erh0qVLWLVqFZ4/f46AgABUrlwZCQkJ2Lx5M3bu3AlfX18pmKXKvHnz8NtvvyExMREzZ87EuXPnMGLECLi6uuL69ev49ttvER0djebNmxfLtFArV66Er68v0tPT0bVrV8yePRt+fn5IT09HcHAw1qxZAwDw8vLC1KlT9VJns2bNMHToUBw9ehQDBw6Eg4MDLly4gKVLl0ojEyZOnKhyUW1Df8YaN24MKysrZGRkYO7cuTAzM4O7u7u0XkXVqlVhbW0tpW3VqhUiIyOxdu1aZGZmYuTIkShXrhxiY2Px888/48iRI/Dx8VEZHCqqwp5XfX2WC3KuNJk3bx527dqFmzdv4osvvsClS5fw/vvvo0qVKoiPj8cPP/yAI0eOAAB8fHykYAxRiSWozElISBAABADR79OLxm4OERERkV4NW/xUjP3xiRi2+Kmxm1IgytdoCQkJxm4OUYEV5T7j+vXr4sqVK+L69evF1DoqqKCgIAFAuLm5FTiv4nOg64+qOi5cuCBcXFw05hs9erRIS0vT27ECEPHx8SrTpKamioEDB2o9lsDAwHx5s7OzxYwZM4SpqanW/La2tno5JoVz586JChUqqK1v1KhRYv369dK/Hzx4kCd/YGCgtE8XZ8+eFU5OTmrrq1+/vrh3757G8yWEEJcuXRKVKlXS2Pe69Fth7d69Wzg4OKit38vLS8TGxhapDuVze/PmTVGzZk219Q0YMEBkZWWpLauon7GC9vOMGTPUlh8WFpYn7dWrV4Wrq6va9J988onWvixI+/R1XvX1Wdb1XGk7B/Hx8aJ27doa+9bX11c8efJE63nRJCwsTG1fasP/l7+5Stp9BqeEIiIiIiIiIqI3Sv369XH58mV88sknqF27NmxsbGBjYwNPT0+MHz8ep0+fxi+//KLT28n6YGNjgx07duDw4cMYPnw4atasCWtra9jb26N27dro378/tm7dKk0fpczU1BRLly7FlStXMHXqVDRu3BhOTk4wNTWFvb096tati4CAAPz666+4f/++Xo+pYcOGUr2enp6wtLRE+fLl4efnh61btyIoKAgvXryQ0ivWvSisRo0a4dy5c/jggw/g5uYGc3NzODs7o0WLFvj6668RFRWl09z6devWxeXLlzFjxgyV7f7ll1+K1E5tevfujQsXLuD//u//4OXlBRsbGzg6OqJZs2ZYunQpzp49Cw8PD73VV7NmTcTExGD27Nnw9vaGjY0NypUrh3bt2klv85uZqZ8kxdCfsSVLlmDt2rVo27YtnJ2dYWpqqjZt7dq1cebMGXz44Ydwc3ODhYUFKlSogO7duyM0NFTlVFH6UpTzqq/PckHOlSbu7u44f/48fvjhB7Rv3x4uLi4wNzdHxYoV0b17d2zatAnHjh2Ds7NzoconepPIhFCzghGVWomJidKiYf0+vYhdi+sZuUVERERE+jN8yTNYOQhkvJBh06dOxm6OzpSv0RISElCtWjUjt4ioYIpynxEbG4vs7GyYmZnlmTuciIrf2LFjsX79elSrVg0JCQnGbk6ZMX/+fGk9Aj6ao9KA/y9/c5W0+wyOsCAiIiIiIiIiKoPS09Px119/AQBatWpl5NYQERExYEFEREREREREVCrduHFD7dv7OTk5+PDDD/H48WMAwMiRIw3ZNCIiIpXUT4BHREREREREREQl1hdffIGoqCj4+/ujZcuWcHV1RXp6Oi5cuIC1a9fizJkzAIBOnTqhZ8+eRm4tERERAxZlQt26dfP8Oysry0gtISIiIiIiIiJtUlNTER8fX6i8tWrVgrm5ufTvq1evIjAwUG16X19fbN++HTKZrFD1vUn0ed6IiMg4GLAgIiIiIiIiInqDREdHw8/Pr1B54+Pj4e7uDgCYNWsWvLy8cODAAdy+fRtJSUnIysqCi4sLmjVrhsGDB8Pf3x8mJqVjxnB9nTciIjIeBizKgMuXL+f5t/LK8ERERESlVU4OUK2asVuhu5wcY7eAiIhKm1q1amH27NmYPXu2sZtCr5k/fz7mz59v7GYQEb1xGLAgIiIiolLr7l1jt4CIiKjgOnTooHaxbFKP542IqORjwIKIiIiIShUT09z/mpoCVasaty0FkZMDPHhg7FYQEREREREZDwMWRERERFSquLoCz1OBSpWBxERjt0Z3iYkAZ+0kIiIiIqKyrHSsqkRERERERERERERERCUaAxZERERERERERERERGR0nBKKiIiIiEql5DSB6b8+M3YzdPby6XNjN4GIiIiIiMioGLAgIiIiolJJCOB5qjB2M3SWmlZy2kpERERERFQcGLAgIiIiolKlnI0JALmxm1Fgpq9kxm4CERERERGRUTFgQURERESlymfvlTN2EwolMTEVqz8xdiuIjMPU1BTZ2dnIycmBEAIyGQN4REREJYUQAjk5OQBy/59OVBRcdJuIiIiIiIiMysLCAkDuA4+0tDQjt4aIiIgKIi0tDULkTm+q+H86UWExYEFERERERERG5eDgIG0/ffpUeuhBREREbzYhBJ4+fSr9W/n/6USFwYAFERERERERGZWdnZ00DVRKSgoSExORmprKwAUREdEbSgiB1NRUJCYmIiUlBQAgk8lgZ2dn5JZRScc1LIiIiIiIiMioTExMULVqVdy9exdCCKSkpCAlJQUymYxzYRMREb2BFOtOKchkMlStWhUmJnw/noqGAQsiIiIiIiIyOnt7+zxBCyD37c3s7Gwjt4yIiIg0UQQr7O3tjd0UKgUYsCAiIiIiIqI3gr29Pby8vJCSkoIXL14gMzMTOTk5xm4WERERvcbU1BQWFhZwcHCAnZ0dR1aQ3jBgUQbUrVs3z7+zsrKM1BIiIiIiIiLNTExM4ODgwEU7iYiIiMoghr6IiIiIiIiIiIiIiMjoOMKiDLh8+XKefycmJqJ69epGag0RERERERW3O3fu4LvvvkNoaCju3LkDS0tLeHh4YNCgQfjoo49gY2Nj7CYSEREREeXDgAUREREREVEpEhoaioCAACQnJ0u/S0tLQ3R0NKKjo7Fu3Tr8/fffeOutt4zYSiIiIiKi/DglFBERERERUSlx/vx5DBo0CMnJybCzs8PChQtx8uRJHDp0COPGjQMA/Pvvv+jZsydSUlKM3FoiIiIiorw4woKIiIiIiKiUmDJlCtLS0mBmZob9+/fDx8dH2texY0d4enpixowZuHbtGr799lvMmzfPiK0lIiIiIsqLIyyIiIiIiIhKgejoaBw5cgQAMGbMmDzBCoWpU6fC29sbALBixQpkZWUZsolERERERBoxYEFERERERFQK/Pnnn9L26NGjVaYxMTHBiBEjAADPnj2TAhxERERERG8CBiyIiIiIiIhKgfDwcACAra0tmjZtqjZd+/btpe3jx48Xe7uIiIiIiHTFgAUREREREVEpcPXqVQCAh4cHzMzUL1dYu3btfHmIiIiIiN4EXHS7DMrOzpa20188QGKio/EaQ0REREQAgPv370vbytdrRLrIyMjA48ePAQDVqlXTmNbJyQm2trZITU1FQkKCznUkJiZq3K9cFu8ziIiIiN4MJe0+gwGLMigpKUna/uenLqj+kxEbQ0RERET5JCUlwd3d3djNoBLk5cuX0radnZ3W9IqARUpKis51VK9eXee0vM8gIiIievOUhPsMTglFRERERERUwmVkZEjbFhYWWtNbWloCANLT04utTURERET0Znn48KGxm6AVR1iUQfXr15ciaWFhYRrntzW0jh07AgAOHz5c6us3RF36ruP+/fto0aIFACAqKgqVK1fWS7lUNhj777u0KUvns6Qe65vabmO2y9B1F3d9+i4/OztbGglbv359vZRJZYeVlZW0nZmZqTX9q1evAADW1tY616Ft+qj4+Hi0a9cOAHDy5MkCjcigkoX3BWUL+7vsYF+XLezvsiMhIQGtW7cGkHctszfVm/OkmgzGysoKNjY2APDGDQEyNzcHoH3e3dJQvyHqKs46KleubLR+opLJ2H/fpU1ZOp8l9Vjf1HYbs12Grru46yuO8t+0azMqOezt7aVtXaZ5Sk1NBaDb9FEKBfmsV69e/Y37/qPiwfuCsoX9XXawr8sW9nfZofySy5uKU0IRERERERGVcFZWVihfvjwA7YtjP3v2TApYcBQEEREREb1JGLAgIiIiIiIqBby9vQEAcXFxyM7OVpvu2rVr+fIQEREREb0JGLAgIiIiIiIqBdq0aQMgd7qnmJgYtemOHj0qbfv6+hZ7u4iIiIiIdMU1LMqoy5cvG7sJKhm7XYas3xB1Gft8Einj51G/ytL5LKnH+qa225jtMnTdxV3fm9rHVHb17dsXixcvBgAEBQWhZcuW+dLI5XJs3LgRAODo6Ag/Pz+DtpGIiIiISBOOsCAiIiIiIioFWrRogbZt2wIA1q9fj4iIiHxpvvnmG1y9ehUAMHnyZGnxeCIiIiKiNwFHWBAREREREZUSK1euhK+vL9LT09G1a1fMnj0bfn5+SE9PR3BwMNasWQMA8PLywtSpU43cWiIiIiKivBiwICIiIiIiKiUaN26M7du3Y9iwYXjx4gVmz56dL42XlxdCQ0Nhb29vhBYSEREREaknE0IIYzeCiIiIiIiI9Of27dtYuXIlQkNDkZiYCAsLC3h4eOC9997DpEmTYGNjY+wmEhERERHlw4AFEREREREREREREREZHRfdJiIiIiIiIiIiIiIio2PAgoiIiIiIiIiIiIiIjI4BCyIiIiIiIiIiIiIiMjoGLIiIiIiIiIiIiIiIyOgYsCAiIiIiIiIiIiIiIqNjwIKIDOL58+f43//+Bx8fH1SqVAmWlpaoWrUqOnbsiN9//x1CCGM3kYiIiIiIiIiIiIxIJviUkIgMIC4uDo0aNUKrVq3g4eEBZ2dnPHr0CCEhIXj06BHGjRuHNWvWGLuZREREREREREREZCQMWBCRQeTk5EAIATMzszy/f/nyJVq1aoUrV67g0qVLqFu3rpFaSERERERERERERMbEKaGIyCBMTU3zBSsAwN7eHt26dQOQOwqDiIiIiN5sKSkpOHbsGL7++msMGjQINWvWhEwmg0wmg7u7e7HUGRERgeHDh8Pd3R1WVlaoXLkyunfvjuDg4GKpj/JLS0vDsmXL0KJFCzg7O8POzg7e3t6YNm0a7ty5o7d6kpKS8OWXX6J169ZwdnaGubk5HB0d0bRpU8ycORO3b9/WW12kmqH6WuHq1auYPn06GjRoACcnJ1hbW8Pd3R2dO3fGokWLcOvWLb3XSbkM3dcKcrkcPj4+0v87ZDJZsdVF/ynu/s7OzsaBAwcwffp0tG3bFhUqVJC+w5s0aYJp06bhxo0bejiSsuvOnTuYNm0avL29YWtrC2dnZ7Ro0QJff/010tLS9FZPcHAwunXrhsqVK8PKygru7u4YPnw4IiMj9VaHRoKISr2HDx+KkJAQMXfuXNG9e3fh4uIiAAgAYuTIkQUq6/bt22Lq1Kmidu3awsbGRjg5OYnmzZuLZcuWidTU1AK3LT09XTRs2FDIZDJx/fr1AucnIiIiIsPq0KGDdC35+o+bm5ve61uwYIEwMTFRW2fv3r1Fenq63uul/8TFxYlatWqp7YNy5cqJ0NDQItdz8ODBPPcqqn5sbGzE1q1b9XBUpIqh+loIIeRyuZgzZ44wMzPT2OfLly/XS32UlyH7+nXff/99vvqoeBV3fz969Ejr9zcAYWFhIVasWKHHIys79uzZI8qVK6f23NaqVUvcuHGjSHWkp6eLXr16qa3DxMREfP7553o6IvU4JRRRGaDpbYWRI0diw4YNOpUTGhqKgIAAJCcnq9xfq1Yt/P3333jrrbfUlvH8+XOsWLECcrkcjx49wt9//42EhAQEBgZi/vz5OrWDiIiIiIynQ4cOOHr0KADAyckJzZo1Q0REBFJSUuDm5qbXt6HXrVuHcePGAQDefvttzJ49G/Xr18e9e/ewcuVKhIWFAQACAgKwefNmvdVL/0lJSUHz5s1x7do1AMC4cePg7+8Pa2trhIWFYfHixUhJSYGNjQ0iIiLQoEGDQtUTHx+PevXqSW+I9unTB8OHD0eNGjVw7949/Pnnn/j1118hhICpqSnCw8Ph4+Ojt+Mkw/W1wrhx47Bu3ToAQMOGDTFmzBg0bNgQ1tbWSEhIwJUrV7Bz506MHj0akydPLvLx0X8M3dfK7t69izp16uDly5dwcXHB48ePAQB8PFl8DNHfiYmJqF69OgCgUaNG6NOnD1q2bImKFSsiOTkZe/fuxffff4+MjAwAwOrVqzF+/Hj9HWQpd/78ebRu3RppaWmws7PDrFmz4Ofnh/T0dAQHB2Pt2rUAgNq1ayM6Ohp2dnaFqicgIABbt24FAPj5+WHy5MmoUqUKLl68iEWLFkkjZNauXYuxY8fq5+BUKfaQCBEZHZSiodWrVxddu3Yt8AiLc+fOCRsbGwFA2NnZiYULF4qTJ0+KQ4cOiXHjxknl1a5dW7x8+VJtOfHx8XnaY25uLpYtWybkcrmejpaIiIiIitPq1avFli1bRGxsrPQ7Nzc3vY+wePbsmXB0dBQARI0aNURSUlKe/dnZ2aJ3797SdeXRo0f1Vjf9JzAwUDrHX331Vb79J0+elN6Q9/PzK3Q9kyZNkuqZPn26yjTfffddnpE1pF+G6mshhFi/fr1U15QpU0ROTo7atK9evSpSXZSfIfv6dX379hUAxPvvvy/at2/PERYGYIj+TkxMFF26dBERERFq00RGRgpra2tpRMeLFy8KVVdZpBjdamZmJk6ePJlv/1dffSX18YIFCwpVx5EjR/L8PzY7OzvP/qSkJFGjRg0BQDg5OYlnz54Vqh5d8BuBqAyYN2+eCAkJEQ8ePBBC5A0a6Bqw0PeXY3Z2toiPjxeLFy8WFhYWol+/fiIrK6tAx0VEREREb4biCFgoX19u27ZNZZqEhARhamoqAIhevXrprW7KlZmZKQWNvL291T5UnjBhgtRXp0+fLlRdjRs3FgCETCYTycnJKtPI5XLh7OwsAAgXF5dC1UOqGbKvX7x4IZycnAQA0aFDh6I0mwrBkH39ut9//10AEOXLlxePHz9mwMIAjNnfqkydOlWqZ9euXcVWT2kSFRUlnbMJEyaoTJOTkyO8vb2lYEJmZmaB6+nRo4cAIExNTUVCQoLKNNu2bZPa8vXXXxe4Dl1x0W2iMmDBggXo1asXKlasWKj80dHROHLkCABgzJgxKodeT506Fd7e3gCAFStWICsrS2OZpqamcHd3x6effoovv/wSf/zxhzSEjYiIiIjozz//BAA4ODigf//+KtNUq1YNnTt3BgAcOHAAKSkphmpemXDkyBE8f/4cQO5UsiYmqh8hjBo1StretWtXoerKzMwEALi4uMDBwUFlGplMJk0/++rVq0LVQ6oZsq+3bNmCZ8+eAQCnBTYCQ/a1shcvXuB///sfAGDZsmVwcXEpcpmknbH6Wx0/Pz9pmwtw60ZxPQQAo0ePVpnGxMQEI0aMAAA8e/ZMeoanq5SUFBw6dAgA0KVLF1SrVk1luv79+0v/jy7OzwkDFkSkVXF/OXbt2hUACvyFSkRERESlU2ZmJqKiogAAPj4+sLCwUJu2ffv2AHIfYEdHRxukfWVFeHi4tK04z6o0a9YMtra2AIDjx48Xqi4vLy8AwJMnT/DixQu16W7evJknPemHIft6x44dAABXV1epLiEEHjx4gBs3bjDwWMwM2dfKPv30U9y9exft2rXL83Ccipex+lsd5WCzuuAJ5aXoQ1tbWzRt2lRtOuX+LWgfRkVFSX2j6XNiYWGBVq1aSXm0vaxcWPxkEJFWxf3leO/ePQCAmZlZIVtIRERERKVJbGwssrOzAeQuIKmJ8v6rV68Wa7vKGuXzqakfzMzM8Pbbb+fLUxATJkwAkPvgetGiRSrT/Pjjj3j69Gme9KQfhupruVwuBRZbtWqFzMxMLFiwAFWrVkXlypXh4eEBBwcHNGvWDJs2beJCzMXAkH/XChEREVi9ejXMzc2xatWqIpVFBWOM/tbk6NGjOrWH/qPoDw8PD43PzYpyPaTr50R5f3Z2NmJjYwtUj64YsCAirfTx5Xju3DkkJyfny/P06VPMnj0bAPDOO+/oo7lEREREVMIlJCRI2+qmJVCoXr26ynxUdIrzaWtrC0dHR41pFf2QlJRUqOmaunXrhpkzZwIAli5digEDBmDXrl04ffo0QkJCMGbMGGk6mYCAAIwZM6bAdZB6hurrhIQEvHz5EkDuCIt27dph/vz5uH//vpRGCIGYmBiMGDEC/v7+UvCS9MOQf9cAkJWVhfHjx0Mul2PatGmoU6dOocqhwjF0f2ty//59BAUFAQDKly+fZ3ooUi0jIwOPHz8GoP16yMnJSRolU9DroTftuouvMxORRoX5ckxNTc33pbVhwwasW7cOfn5+cHNzg62tLW7fvo3Q0FCkpKRgwIABGDp0aLEdBxERERGVHIoHmgBgZ2enMa3i5hwAp5LRM0U/aOsDIH8/WFpaFri+JUuWoGPHjli8eDF27dqVb37sxo0b49NPP8WgQYMKXDZpZqi+VoyQAYBNmzbh1atX8Pb2xrJly6QR+0eOHMG0adPw77//4rfffkPNmjWxZMkSnesgzQz9d7106VJcunQJNWvWxNy5cwucn4rG0P2tjhACH3zwgdSeuXPnwtraWm/ll1YFuR4CID2TK+j10Jt23cWABRFppK8vx4EDByI5ORmRkZE4duwY0tLS4OzsjDZt2khvzshkMr23n4iIiIhKnoyMDGlb0/oVAPI8UElPTy+2NpVFin7Q1geAfvrh/v372LhxIyIjI1Xuv3jxIrZs2YIGDRpwKhE9M1Rfp6amStuvXr1C9erVcfz4cTg7O0u/79WrF1q1aoWGDRvi3r17WL58OT7++GNUrVq1QHWRaob8u46NjcXChQsBAD/88AMfUBuBob/H1Vm0aBF2794NIHfh7UmTJum1/NKqINdDwH99WND+e9OuuzglFBFppK8vxzZt2iAoKAhXr15FcnIysrKy8PDhQ+zduxdDhgxhsIKIiIhIj7KzsyGTyYr8s2HDBqO038rKStrOzMzUmFZ52oqy+jCsuPpb0Q/a+gAoej9cvXoVLVq0wJYtW2Bra4uffvoJCQkJyMzMxIMHD7Bx40ZUqVIFu3fvho+PT56FZMuSkt7Xyn/bADBnzpw8wQqF8uXLS1MHZ2Zm5httUxaU9L4GcteaycjIwIABA9CjR48C5y9LSkN/q7NlyxZpdI27uzu2bt3KBbd1VJDrIeC/PizKd/ObcN3FERZEpJGhvhyJiIiIiBTs7e2lbW3TDSi/sa3LiGDSnaIfdJnyoaj9MHz4cCQmJsLGxgbh4eHw9vaW9lWsWBHDhw9Hp06d0KRJEzx8+BBDhgzBjRs39DplSVlmqL5W/tsGNK9j2K1bN2lbsVA3FZ2h+vqXX35BWFgY7O3tsXLlyoI1kvTGkN/jqoSGhmL06NEQQqBixYo4cOAAKlWqpJeyy4KCXA8B//VhUb6b34TrLgYsiEgjQ305EhEREZH+mJmZ4erVq0Uup3LlynpoTcEpr52WmJioMa3y2mnKC0GWJcXV39WqVcOpU6eQmpqK58+fa1ywVdEPFSpUKHAQ4fz584iJiQGQu6C2crBCWZUqVfDxxx/js88+w927d/HPP/+gT58+BaqrpCvpfV2tWjXIZDIIIQBA4zRPyn/Pjx49KlA9pUFJ7+ulS5cCANq3b692RJRyvwYHBwPInWa6d+/eBaqrNCjp/a3KkSNHMHDgQGRlZcHJyQn79++Hh4dHkcstS6ysrFC+fHk8fvxY6/XQs2fPpGdyBb0eev26q1mzZmrTGuK6iwELItLIUF+ORERERKRfJXmOfy8vL5iamiInJwfXrl3TmFZ5v7oH3WVBcfR3nTp18PvvvwPIPc+tWrVSmS47Oxs3btwAULg+UH5I16RJE41pmzZtKm1fu3atzAUsgJLd17a2tnBzc8OtW7cAAHK5HKampirT5uTkSNtmZmXz8VVJ7mvF7At79uzBnj17tKYfMmQIAMDNza1MBiyAkt3fr4uKikLv3r2RkZEBOzs77N27Fw0aNChyuWWRt7c3wsPDERcXh+zsbLXfh0W5HqpTp47KcjTVY2ZmVmwBKE4YRkRaKb7oFF+O6vBmkYiIiIj0wcLCAi1atAAAREREaJya9OjRowBy11LT9EYgFVybNm2kbcV5VuX06dPSi0u+vr4Frkf54Yum+w0AyMrKUpmPisZQfQ0A7dq1k7YVD0hVUd7HBbf1x5B9TcZnjP6+cOECunfvjpSUFFhZWSEkJAQtW7YsUpllmaIPU1NTpdGIqij3b0H7sHnz5tK6tZo+J5mZmYiMjMyXR98YsCAirQzx5UhEREREpKxv374AgBcvXqhdcDcxMREHDx4EAHTq1Cnf/PhUNB06dEC5cuUAAL/++qs0jc/rlBd57devX4HrqVmzprStbTFt5XsO5XxUNIbqawAYMGCAtK1pMW3lfW3bti1UXZSfofr61q1bEEJo/Gnfvr2UXvE7xegb0g9D/m0DwPXr19G1a1c8e/YM5ubm+P3339GhQ4dCl0f/XQ8BQFBQkMo0crkcGzduBAA4OjrCz8+vQHXY29ujU6dOAICDBw+qnWFl165dePHiBYCifU60EkRU5sTHxwsAAoAYOXKk1vSnTp2S0k+YMEFlmpycHOHt7S0ACEdHR5GZmannVhMRERHRm8rNzU0AEG5ubjqlV74ebd++vco0T548EeXKlZPKffz4cZ792dnZonfv3lI5hw8fLuJRkCpz586VzvFXX32Vb//JkyeFmZmZxr7U1t85OTmiatWqAoAwNTUVBw8eVFlOTEyMsLW1FQCEjY2NePbsWRGOjF5niL4WIre/69WrJwAIBwcHce3atXxprly5Iuzs7AQAUaFCBZGamlqUQ6PXGKqvtWnfvr1UBhUfQ/X37du3RfXq1aXv8h07dujxKMq2tm3bCgDCzMxMnDx5Mt/+r776SuqfwMDAfPuDgoI07hdCiEOHDklp3n33XZGdnZ1nf1JSkqhRo4b03O/p06f6ODSVOH6SiLRq0aIF2rZti/DwcKxfvx4jR46Ej49PnjTffPONNPfs5MmTYW5uboymEhEREVExi4uLw/Hjx/P8LiUlRfqv8luaANC9e3dUqlSpwPU4Oztj6dKl+OCDD3D79m20bNkSc+bMQf369XHv3j2sWLECYWFhAHLnPi/o24Skm+nTp2P79u24fv06ZsyYgbi4OPj7+8Pa2hphYWFYtGgRsrOzYW1tjRUrVhSqDhMTEyxevBgjRoxATk4O3nnnHUyYMAG9e/eGq6srnj59iv379+P7779HWloaAGDWrFkaF4+lgjNEXwO5/b1q1Sp06tQJL168QOvWrTFz5ky0b98eQggcPXoUS5culb5Xvv/+e9jY2OjpKAkwXF/Tm8EQ/f3kyRN07txZWpB56tSpqF27Ni5duqQ2j5OTE6d709HKlSvh6+uL9PR0dO3aFbNnz4afnx/S09MRHByMNWvWAMhdA2zq1KmFqqNjx47w9/dHcHAwdu/ejS5dumDKlCmoUqUKLl68iIULF+LOnTsAgCVLlsDJyUlvx5dPsYVCiOiNER4eLoKCgqSfZcuWSVFTX1/fPPuCgoJUlnHmzBlhbW0tAAg7OzuxaNEiERERIQ4fPizGjx8vlefl5SVevHhh2AMkIiIiIoNRfktPl5+wsLB8ZRTkzdx58+YJmUymtvwePXqI9PT04jlYEkIIERsbKzw9PdX2gYODgwgJCVGbX9f+XrZsmTA3N9f4eZLJZGLKlClCLpcXw5GSofpaCCF27twp7O3t1dZlbm4uVq1apecjJAVD9rU6HGFhOMXd32FhYQW6NoCOM37Qf3bv3i0cHBzUnk8vLy8RGxurMq8uIyyEECItLU306NFDbR0mJiYa8+sLR1gQlQHr1q3Dr7/+qnLfiRMncOLEiTy/GzVqVL50jRs3xvbt2zFs2DC8ePECs2fPzpfGy8sLoaGhnDuYiIiIiPRmwYIF6NatG3788UeEh4fj4cOHcHR0RMOGDTF69GgMGTLE2E0s9Tw8PHD27Fn8+OOP2LFjB+Li4pCZmYnq1aujR48emDx5Mtzc3Ipcz7Rp09C7d2+sWbMGR48eRVxcHFJSUmBjYwM3Nze0adMGY8eORdOmTfVwVKSKofoayF3Lonnz5vj+++8RGhqKhIQEyOVyVK9eHZ07d8aUKVPg4eGhl7ooP0P2NRkf+7vk6927Ny5cuICVK1ciNDQUiYmJsLCwgIeHB9577z1MmjSpyKPRrK2tERoaiq1bt2LDhg04f/48nj9/jooVK6Jt27aYNGlSvhlXioNMCDWrrRBRqTFq1Ci1AQtVNH0t3L59u1i/HImIiIiIiIiIiKhsYsCCiIiIiIiIiIiIiIiMzsTYDSAiIiIiIiIiIiIiImLAgoiIiIiIiIiIiIiIjI4BCyIiIiIiIiIiIiIiMjoGLIiIiIiIiIiIiIiIyOgYsCAiIiIiIiIiIiIiIqNjwIKIiIiIiIiIiIiIiIyOAQsiIiIiIiIiIiIiIjI6BiyIiIiIiIiIiIiIiMjoGLAgIiIiIiIiIiIiIiKjY8CCiIiIiIiIiIiIiIiMjgELIiIiIiIiIiIiIiIyOgYsiIiIiIiIiIiIiIjI6BiwICIiIiIiIiIiIiIio2PAgoiIiIiIiIiIiIiIjI4BCyIiIiIiIiIiIiIiMjoGLIiIiIiIiIiIiIiIyOgYsCAiIiIiIiIiohLh6NGjkMlkqFChAlJSUozdHIO4desWZDIZZDIZNmzYUKSyRo0aBZlMBnd390KXkZiYCEtLS1hYWOD69etFag8R0esYsCAiIiIiIiIiojeeXC7H5MmTAQBTp06FnZ1dnv3u7u7Sg/3C/JBuqlWrhtGjRyMrKwtTp041dnOIqJRhwIKIiKiUuHv3rnSzFR0dXaSyTp06hdmzZ6N169aoVq0arK2tYWNjg6pVq6Jjx46YOXMmTpw4ASGEnlqfV7du3aRjuXnzZoHyrl+/Xsr79ddfF0v7iIiIiMjwtm/fjvPnz8PFxQWTJk0ydnPeGBs2bJCuf2/dumWQOmfNmgVzc3Ps2bMHERERBqmTiMoGM2M3gIiIiPRjz549AIBKlSqhWbNmhSrj33//xccff4wDBw6o3J+eno579+4hLCwMX331FTw8PDB37lyMGDGi0O1WZcSIEdi/fz8AYPPmzZg3b57OeTdv3gwAMDExwdChQ/XaLiIiIiIynoULFwIAJkyYkG90BQDs378fmZmZOpf3wQcf4MSJE1KZbyp3d/die1GosNzc3DBgwAAEBwfjyy+/RGhoqLGbRESlBAMWREREpURISAgAoFevXoUa0n7gwAG89957SE5OBgC8/fbbeO+99+Dj4wNXV1eYmpri0aNHOHfuHPbt24fw8HDExcVh/Pjxeg9Y9OvXD/b29nj58iU2bdqkc8Dizp07OHr0KACgc+fOqFKlil7bRURERETGceDAAVy+fBkAMGzYMJVpvLy8dC7vxx9/lIIVjRs3xsqVK4veyDJm6NChCA4Oxt69e3H9+vUCnX8iInU4JRQREVEpkJaWhsOHDwMAevfuXeD8V65cQb9+/ZCcnAxTU1N8++23uHbtGhYvXox3330XrVq1QvPmzdGzZ0/MmTMHx44dw9mzZ9GzZ099HwoAwMbGBgMHDgQAxMXFITIyUqd8W7Zskd4+03cQhYiIiIiMZ/369QCAJk2awNvbu0hlnT59Gp988gkAoFy5ctixYwcsLS2L3Maypnv37nBxcYEQAkFBQcZuDhGVEgxYEBERlQIHDx5Eeno6rKys0Llz5wLlFUJg6NChSE1NBQAEBQXh//7v/2BmpnkgZqNGjbBnzx6sXbu20O3WRDngsGnTJp3yKNLZ29ujX79+xdIuIiIiotLgxIkTGDt2LGrVqgUHBwfY2dmhdu3a6Nu3LzZu3IgXL16ozBcSEoKBAweiWrVqsLS0hIuLC3x8fLBkyRKkpKRorPP58+dYuHAhfHx84OTkBHNzc1SoUAF16tRBv379sGrVKjx69ChfvoyMDOzevRsAMGDAgCId97Nnz/Dee+9JU0etX78eb7/9tsY8aWlpWLFiBfz8/FCxYkVYWFjA1dUVXbt2RVBQEHJyctTmVSwEPmrUKADAtWvXMG7cOLi7u8PS0hIVK1ZEv379NL6gc+vWLWmNig0bNki/P3LkCGQyGUaPHi39rmbNmvkWEz9y5Ijasp8/f4558+ahbt26sLW1haOjI9q1a4ctW7ZoPCcAYG5uLr0stX37dq3piYh0IoiISqDAwEABQCi+xpKTk0VgYKCoV6+esLW1Fa6uruKdd94RJ06cyJPv4cOHYs6cOaJOnTrCxsZGODs7i3fffVecOXNG57rUCQsLk9KFhYWpTffo0SMxZ84c0ahRI1GuXDlhaWkp3NzcxLBhw0R4eLjGOtzc3AQAMXLkSCGEEFevXhVjx44Vbm5uwsLCQri6uoq+ffuKiIgIjeUUxLVr18THH38s6tSpIxwcHISVlZWoWbOmGDVqlIiJiVGbT9X52L59u+jYsaMoX768sLKyEl5eXmL69OniyZMnOrVl//79IiAgQLi7uwsrKythb28vGjRoIKZPny7u3bunNt/rffj8+XPx+eefS30AQAQFBeXJk5SUJKZNmyY8PT2FlZWVcHV1FZ07dxa7du0SQggRFBQklRkfHy/l++uvv6TfBwcHaz2mTz75RAAQJiYm4s6dOzqdB1XGjRsnAIgePXoUOO/u3bulNvfp06fQbVBHLpeLHTt2iP79+4tq1aoJS0tL4ejoKJo3by4+//xz8ezZM7X5FJ95FxcXkZmZqbGe06dPS8cxatQovR8HERERUWmQlpYmhgwZIl03qfsJDAzMky89PV3069dPY54qVaqIs2fPqqz3ypUrokqVKlrr/f777/PlPXLkiLT/0KFDhT52uVwuevfuLZU1efJkrXmioqJE1apVNba5RYsW4sGDByrzK9/D/f7778LGxkZlGaampmrvH+Lj46V0yvctyvdcmn6U709HjhwpAAg3Nzdx9epV4e7urjbfxIkTtZ6fn3/+WUp/+/ZtremJiLRhwIKISiTlB9B37twRXl5eai/6fvvtNyGEEOfPn1d7oWlpaan2wlefAYt9+/YJBwcHjReTEydOFDk5OSrz6+NityA+//xzYWZmpratMplMzJs3T+v5OHjwoBg6dKjacjw8PMT9+/fVtiMlJUXrzZGdnZ0ICQlRmV+5D69fv67yolz5wv/cuXOiQoUKausaP3682oBFdna2dCPWrVs3jec3KytLuLq66pRWE7lcLtW5atWqAufv37+/1s9uYT169Ej4+vpq7LuKFSuKyMhIlfnnzJkjpfvzzz811jV58mQp7eHDh/V6HERERESlQU5OjujSpYt0zeTp6SmWL18uwsPDRUxMjNizZ4+YPXu28PDwyBewGDRokJSvYcOGYuPGjSI6Olrs27dPjB49WshkMgFAODs7i8TExHx1N23aVAAQ5ubm4qOPPhIhISEiOjpanDp1Svzxxx9i1qxZwsvLS2XA4osvvpDuP54/f17o41+yZIl0DC1bttT6QsyFCxeEra2tACBcXV1FYGCgOHjwoDh79qzYt2+fmDhxonS/pK48xT1c48aNpZe/fvjhBxEZGSkiIiLE/PnzhZWVlQAgHBwcxKNHj/KVoS5gkZKSIi5evCi+/PJLaf++ffvExYsX8/ykpKRIeRQBiwoVKghPT09hb28vPvvsM3HkyBFx+vRpsXbtWlGtWjWpvH/++UfjOTpz5oyUdvPmzVp6gIhIOwYsiKhEUn4A3bJlS2FjYyNmzZoljh49KqKjo8Xy5culwIC9vb24efOmqF69unB2dhYLFy4Ux48fF6dOnRILFiwQFhYWAoCoUaOGePXqlca6NNEWsDh79qxUl7m5uZgyZYoICwsTUVFRYvXq1aJmzZpS/hkzZqisQx8Xu7qaO3eu1J7WrVuLdevWiYiICHH69GmxZcsW4ePjI+3/7rvvNJ6P1q1bCwCib9++YteuXSImJkb8/fffomfPnlIaf39/le3Izs4Wfn5+0g3KkCFDxI4dO8Tp06dFRESEWLlypahRo4YAICwsLMTp06fzlaHchw0aNBDm5ubi448/FgcOHBCnT58W27ZtEydPnhRCCPH06VNRqVIlKX1AQIDYu3evOH36tAgODpaOu2XLlioDFkIIMWvWLAHkjppISEhQe47//PNPqYzt27cXoHfyio6OlsrRVJ8qcrlcCs7Y2dmpDZYVRkpKivD29pb6ZsKECeKvv/4SZ86cEeHh4WLhwoXCxcVFABBOTk7i1q1b+cr4999/pWMbMGCA2rqUgz81atQQcrlcb8dBREREVFqsWLFCurbq16+fyMjIUJkuJydH3L17V/r3nj17pHydOnVSed+0Zs0aKc2gQYPy7Ltx44a0T1VAQkEul4unT5/m+/0777wjAIi3335b10PN5+jRo8LU1FQKqmgbDSCXy0WDBg2kAE1SUpLKdHv37hUmJiYCgFi3bl2+/Yp7OACiadOmKgMumzdvltJ8++23+farC1goqHuZShVFwAKAcHR0FJcuXcqXJjY2VrqvfPfddzWWl5WVJczNzQUA8dFHH2lMS0SkCwYsiKhEUn4AbWlpqfLt7NDQUClNhQoVRPny5UVcXFy+dD/++KOUTjHdj7q6NNEWsGjevLkAckc/7Nu3L9/+p0+fijp16kgPulVdOOrjYlcXUVFR0kX3Z599pjJNTk6OGDZsmBQUen1an9eHJ3/55Zf5ypDL5aJr164CgDAzM1MZYPn666+lIM/ff/+tsi1Pnz4VdevWFQBEmzZt8u1X7kMTExOxf/9+tcf+v//9T0r79ddf59ufnZ0t+vTpk+fYXr8puHHjhvSG2cKFC9XW9e677wogd7ojdTeLupg3b54AIBo1alTgvHfv3pWOw9fXt9BtUGXSpEkCgChXrpyIjo5WmebWrVuicuXKAoAYNmyYyjStWrWS/tbVTR+l/Pc+e/ZsfR0CERERUamRk5MjjTivWrWqePnypc55FQEDc3NzjdOYdu7cWbq2V56y9cSJE9K12vnz5wvc9vr16wsAwsfHp8B5hRDiwYMH0jWnTCYTe/bs0ZonJCRE5zYrRp+oup5WvodTV47yiOl+/frl219cAQtVL54p+Pv7Sy8WaVOxYkWdghtERLrgottEVOJNmTIFLVu2zPf7Hj16wM3NDQCQlJSEL7/8UuViaqNHj4aVlRUAIDw8vFjaGBUVhejoaADA2LFj0bVr13xpnJycsGbNGgCAXC7HTz/9pLHMX375BeXKlcv3+6FDh6JKlSoACn88S5cuhVwuR9OmTfH555+rTGNiYoLvv/8elpaWePnyJXbu3Km2vKZNm2L27Nn5fi+TyfDJJ58AALKzsxEREZFnf1ZWFr755hsAwKRJk/DOO++oLN/JyQnLli0DABw/fhxxcXFq2zJq1Ch06dJF5b6MjAz8+uuvAIAmTZpIbVNmamqK1atXS58ZVd566y34+fkBQJ5F8ZQ9evQIf//9NwAgICAAlpaWasvTZs+ePQAgLXhXEI8fP5a2K1SooDHtrVu3cOnSJZU/ry+O+PjxY6xbtw4A8Pnnn6NZs2Yqy3Rzc8PcuXMB5C7Ul5aWli+NYvHtV69e4bffflNZjvKi3MqLdRMRERFRrnPnzuHu3bsAgHHjxsHOzk6nfNnZ2Th69CgAoEuXLqhevbratOPGjZPyKC/0XLlyZWlb3fWxJklJSQByr/sLSi6XY+jQobh//z4AYMaMGejZs6fWfH/99RcAoFatWmjQoIHGtO3atQMAREdHq12Au379+mrLkclkaNy4MQDg5s2bWtumDzKZDEOHDlW7v2nTpgByFyl//vy5xrKcnZ0B/NdPRERFwYAFEZV4/v7+avcpLghlMhkGDRqkMo21tTU8PT0BFN/F4cGDB6XtMWPGqE3n6+sLb2/vfHleV5wXu1lZWdi7dy8AYODAgZDJZGrTOjo6on79+gCQL9igbOjQoWrLUVwIq2pvVFSUdGOhrv8UFDcJ2toSEBCgdl9MTAySk5MB5D70VtfmihUrolu3bhrbM3bsWABAbGwsjh8/nm//pk2bkJ2dDQB4//33NZalyd27d3HmzBkAhQtYvHz5UtrWdtM6duxY1K9fX+XP6wG2ffv2ISMjA4DufZeVlYWYmJh8+/39/aWAjnJgQvkYFDeULVq0QK1atTTWR0RERFQWnT17VtpWvnbW5ubNm9JLJapeFFOmvP/SpUvSds2aNdG2bVsAwPLly1G3bl3MmzcPhw8fVvnCyuuePn0KoHABi8DAQBw+fBgA0LZtWyxcuFCnfKdPnwYA/Pvvv5DJZBp/Jk2aBADIzMyU2vq62rVra6xP8dBf+fq8OJUvXx4uLi5a2wNob5OiX548eaKfxhFRmcaABRGVeF5eXmr3OTo6Asi9GNN0catIV1wXh4qLdQsLCymYoI7iIj82NhaZmZkq0xTnxe6VK1ekm4ZZs2ZpvThXXMg/ePBAbZma2qvpQlhRNgD4+PhobIfyw3ZNbdH0dpTyTZVyIEUVdSMGFPr37y8dW1BQUL79it81adIEDRs21FiWJorRFZUqVdLaJlXs7e2l7dTU1EK343XKfVe5cmWNfVevXj0praq+c3JyQq9evQAAJ06cQHx8fJ79O3fuRHp6OgBg+PDhejsGIiIiotJEeWSt8ogHbZQfwFesWFFj2kqVKqnMBwDbtm2Dj48PgNx7ji+++AKdOnWCo6Mj2rdvj59//ll64eV1itHNims+Xf3zzz9SgMLV1RXBwcEwNTXVKe/rI4h1pS4AY2NjozGfiUnuIzp1IzT0Tdf2ANrbpOgXa2vrojeMiMo8BiyIqMTTdKGluMgy9sWh4mLd2dkZZmZmGtMqLvKFEHj27JnKNMV5PPq+MAd06yMgf3uLoy2aAlfK59vV1VVjHdqmT7K0tJQenv/22295ggFRUVG4fPkyAM0jbnQREhICAOjZs6fG0TDqKL9VpW0I98GDByFy17+CEAJhYWFq0+q77xTTPAkhsHnz5jz7FKMuzM3NMWTIkELVS0RERFSWFOa6sSj5AKBq1ao4efIkDh48iI8++gh169aFTCZDVlYWjh07hg8//BD16tXD9evX8+VVXHurG72gSkJCAoYNGwYhBExMTLBlyxZp6lxdKO5NfH19cfHiRZ1/ClJHaaHoF233SEREutD81IyIiPRKlwt8IYQBWqKectBg2bJl6N69u075bG1ti7UtR44c0ThkWZmmYIOub1Tpw9ixY7Fy5UqkpKRg586dGDlyJID/RldYWVlpnDdWm/T0dGl4e2GmgwKAKlWqoHz58nj8+DHOnz8PuVyeJ4hUWIq+s7CwUDnNkzrVqlVT+ft33nkHFSpUQFJSEjZt2iSte5GQkCDNqdyjRw+dPyNEREREZU358uWl7Xv37uk8jabyiGhNI5lf36+cT1mnTp3QqVMnALlTCB08eBBr1qzB4cOHcePGDQwePDjP9FVA7oPwGzduqH2h63VZWVkYPHiwNEXRvHnz0LlzZ53yKri4uODhw4dISkrKMyKY8lP0CwMWRKQPDFgQEWmh/PBW08NcTdPpKC7Wnzx5guzsbI2jLB4+fAggN7hRmDlai0r5gW9WVpZRL86V22JhYVHsbVE+348ePdI43ZguC8rVq1cPLVu2xKlTpxAUFISRI0ciIyMDwcHBAIB+/fpJ05EVxsGDB5Geng4rK6sC34ApyGQytGvXDrt27cLLly9x4sQJaX7holD0XWZmJlxcXAo07YAqitET3333HWJjY3Hq1Cm0bNkSW7ZsgVwuB8DFtomIiIg0adKkibR97Ngx+Pn56ZTvrbfego2NDdLS0nDq1CmNaaOioqRtXa7dXVxcMHjwYAwePBh9+vTB7t27ce7cOcTGxkrrDAK5a/hFRkbixo0bOr1gM2PGDGldu86dO0svuxRE48aNceXKFVy/fh23b9+Gm5tbgcswlKKMfCmqR48e4cWLFwAgrW9IRFQUnBKKiEgL5Tn+Nb3R8++//6rdp7hYz8zMzPe20OsUF/menp6wsLAoSFP1om7dulK9+/fvN3j9ypTX+zBEW+rWrSttK6/BoIq2/QqKxbePHTuGmzdvYteuXXj+/DmAoi22Dfw3HVTHjh2LNMJFMfIDAL777rsitUmhOPpOOSChmAZKMT2Us7OztM4FEREREeXXsGFDVK9eHQCwbt06pKSk6JTPzMwM7du3BwAcOHAACQkJatOuW7cOQO6o5g4dOhSofYpRF0De9TYASC/UpKSk4OrVqxrL2bVrF1asWAEgdzTxli1bCjWC+N1335W2v/rqqwLnNyTFGh8A8OrVK4PWrRyk0seLT0REDFgQEWlRs2ZNaVvTQ+pt27ap3af89vv69evVpouIiMCVK1fy5TEkGxsb6WbhyJEjeS5ADa1NmzbS6JSff/5ZenOnuDRr1gzlypUDkPtAXN30XA8fPsS+fft0KtPf3x92dnYQQmDDhg3SdFDu7u55bsoKSgiB0NBQAIWfDkqhd+/e0mLkO3fuxG+//Vak8oDcKZzMzc0BAMuXL0d2dnaRy2zatKkUVNq+fTsiIyOltUAGDx5slAAfERERUUlhYmKC6dOnAwASExMxYsQIZGZmqkwrl8tx79496d8TJ04EkDsC+/3331eZ75dffpFeVBkwYECeEbbnzp3DuXPn1LZNCIGDBw8CyB0t4O7unme/8oNwTfcncXFxGD16NIDcoElwcLDWtenUGTBgALy9vQEAq1at0ngfBwCXLl2SXigyNOVzfePGDYPWregPS0tLNG/e3KB1E1HpxIAFEZEWvr6+0hROy5cvV/kQe8mSJRqDGS1atJAu3tatW4cDBw7kS5OcnIwJEyYAyL2Z+PDDD/XR/EKZM2eONKzY399f40VvTk4Otm7disTERL23w8rKCtOmTQOQOx+uv7+/xqm3Xr58iR9++KFI9Sne4j9z5gy+/fbbfGnkcjkmTJiAjIwMncq0s7ODv78/AGD16tXSmhOjRo0q0tDtmJgY6SayqCMLZDIZtmzZIi2OPmzYMPz0009aF23XNOKoatWq0s3i+fPnMWHCBI1Bi0ePHklv5Gmi6J/Hjx9Lo1eUf09ERERE6k2cOBFdunQBAPzxxx+oX78+Vq5ciRMnTuDs2bPYu3cvAgMDUbt2baxZs0bK17NnT7z33nsAcqclbdmyJTZv3oyYmBgcPHgQY8eOla7NnJ2d811Hnzt3Do0bN0aLFi3wxRdfIDQ0FDExMYiMjMS2bdvQrVs36WF/nz598k0n6u7ujjp16gAADh06pPLYMjIy8N5770kvOY0aNQpOTk64dOmSzj/K9xqmpqbYvn279PLR2LFj0b17d2zcuBGnTp3CmTNn8M8//2Dx4sXw9fVF/fr1pbXVDK1x48bSKIu5c+di//79uH79OuLi4hAXF4f09PRiq1vRH507d4alpWWx1UNEZYggIiqBAgMDBQCh7Wts5MiRAoBwc3PTmK59+/YCgGjfvr3K/f7+/lJ9vXr1Env37hVnzpwRf/75p+jXr58AIHx8fKQ0YWFh+co4e/assLCwEACEubm5+OSTT0RYWJiIjo4Wa9asEW+99ZaUf8aMGSrb4ebmJgCIkSNH6uW4NVE+x3Z2dmLy5MkiNDRUnDlzRkRERIht27aJ//3vf6JKlSoCgLh48WKe/GFhYRrPhzJFusDAwHz7srOzRadOnaQ0NWrUEIsWLRJhYWHi7Nmz4tixY2Lt2rUiICBA2NraChcXF43Hos2TJ09EpUqVpPQBAQHin3/+ETExMWL79u2idevWAoBo0aKFlObWrVsay4yMjJTSAhAymUxrHm0Ux9SoUaMilaNs7969wsHBQWqnl5eXmDNnjggJCRGnTp0SZ86cEYcPHxY//fSTePfdd4WpqamUdunSpfnKe/nypahXr56Upk6dOmLFihUiPDxcnD17VoSFhYkffvhB9O3bV1hYWIimTZtqbePdu3eFiYlJnvPp5eWlt3NAREREVNqlpqaKgQMH5rmeUvXz+rV5enq6dO+j7qdKlSri7Nmz+eoMCgrSWh8A0aZNG/HkyROV7V68eLF0b5Kamppv/5EjR3SqQ9OPqvuW8+fPC09PT53yL1iwIF9+fdzDxcfHS3UEBQWpzD9jxgydjkvXe0XlPouPj1eZ5tatW0ImkwkAYtu2bRrLIyLSFRfdJiLSwYoVKxATE4PY2Fjs2bMHe/bsybN/0KBBGD9+vMZpnBo1aoSQkBDprZ9vv/1W5Rv8EydOxOLFi/V+DAU1f/58ODo64tNPP0VKSgpWrlyJlStXqkxrYWGRZ95UfTI1NUVISAg++OADbNy4EXfu3MHs2bPVpi/skG8FZ2dn/PPPP+jSpQuSkpKwZcsWbNmyJU+aUaNGoW3bttLwZ23H3rJlS9SvXx8XL14EkPv2UVEX7VO8gVbU6aCUde/eHZGRkZg0aRIOHz6M69evY+HChRrz1KxZE4GBgSpHONjZ2eHo0aMICAjAP//8gytXrmDKlClqy3JwcNDaxipVqqBTp055RikNHz5caz4iIiIiymVjY4MdO3YgLCwMQUFBOH78OB48eAAzMzNUrVoVderUwcCBA/Os4QDkXvPu2rULISEh2LBhAyIjI/H48WPY2trCy8sLffv2xaRJk2BnZ5evzqFDh8Ld3R0HDhxAeHg4EhMT8fDhQ2RnZ8PV1RVNmjSBv78/Bg8erHa9iTFjxmDBggVISUnBX3/9hSFDhuTZL9RM51pUDRo0wJUrV7B161b88ccfiImJQVJSEuRyOVxcXFCrVi20adMG/fr1y7OwuaEtWbIEnp6e2LhxIy5fvozk5GStI6aLauvWrRBCoGLFiujfv3+x1kVEZYixIyZERIVh6BEWQgjx9OlTMXPmTOHp6SksLS2Fs7OzaNeundi0aZMQQvcRBY8ePRKzZ88WjRo1Eg4ODsLS0lLUqFFDBAQEiPDwcI3tNOQIC4XExEQxd+5c0apVK1G+fHlhZmYmbG1thZeXlxgwYID4+eefRVJSUr58+hphoez06dPiww8/FHXr1hXlypUTZmZmwtHRUTRq1EiMGTNG7Ny5U2RkZOTLV5ARFgpJSUli6tSpUn+XL19e+Pn5ia1btwohhFi+fLlUZnp6utbyli5dKqUv6ttHiYmJ0ptMUVFRRSpLnYiICDFz5kzRsmVLUaVKFWFpaSmsra1F5cqVRZs2bcT//d//iUOHDgm5XK5TeYcOHRKjR48Wnp6ews7OTpiZmQlnZ2fRvHlzMXHiRPH333+L7OxsncravHlzntEq6t74IiIiIqLSZcKECQKA6NSpk7GbUubl5OQILy8vAUAsXLjQ2M0holJEJkQxhaCJiIhKsbFjx2L9+vWoVq0aEhIStKYfPnw4Nm/eDCcnJ9y/f79I87uuXr0aH3zwASpVqoR79+4VaS0MIiIiIqKSIiEhAR4eHsjMzMTJkyfh4+Nj7CaVWdu2bcPQoUPh4uKC+Ph42NvbG7tJRFRKcNFtIiKiAkpPT8dff/0FAGjVqpXW9MnJyfj9998BAAEBAUVejE4xJVnPnj0ZrCAiIiKiMqN69erSFKMLFiwwbmPKMCGENHXs/PnzGawgIr3iGhZERESvuXHjBt566y2VwYCcnBx8+OGHePz4MQBg5MiRWsv78ccfkZ6eDgCYMGFCkdvXtm1bNG3aFH369ClyWUREREREJclnn30GGxsbAEBKSorKNTOoeN2/fx8DBw7E0KFD8eGHHxq7OURUynBKKCIioteMGjUKUVFR8Pf3R8uWLeHq6or09HRcuHABa9euxZkzZwBAWvz59cBGdnY2bt26hVevXuHYsWOYOnUq0tPT0atXL2mxbCIiIiIiIiIiyosjLIiIiFS4evUqAgMD1e739fXF9u3bVY7CSExMhKenZ57fOTg4YPny5XpvJxERERERERFRacGABRER0WtmzZoFLy8vHDhwALdv30ZSUhKysrLg4uKCZs2aYfDgwfD394eJifaloFxdXeHj44OFCxfCw8PDAK0nIiIiIiIiIiqZOCUUEREREREREREREREZnfZXQ4mIiIiIiIiIiIiIiIoZAxZERERERERERERERGR0DFgQEREREREREREREZHRMWBBRERERERERERERERGx4AFEREREREREREREREZHQMWRERERERERERERERkdAxYEBERERERERERERGR0TFgQURERERERERERERERseABRERERERERERERERGR0DFkREREREREREREREZHQMWBARERERERERERERkdExYEFEREREREREREREREbHgAURERERERERERERERkdAxZERERERERERERERGR0DFgQEREREREREREREZHRMWBBRERERERERERERERGx4AFEREREREREREREREZHQMWRERERERERERERERkdAxYEBERERERERERERGR0f0/6AjpH1biK2cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_MC = hist_MC_conv_nominal+hist_MC_astro_nominal+hist_MC_pr_nominal\n",
    "\n",
    "filepath = \"../systematics/gradients/ice_grad_0_1.00.json\"\n",
    "effect_ice_grad_0 = nsigma_variation_factor(filepath, 2)\n",
    "hist_varied_ice_grad = hist_MC * effect_ice_grad_0\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(8, 3), dpi=200)\n",
    "\n",
    "ax1.stairs(np.sum(hist_MC,0), energy_binedges, edgecolor='blue')\n",
    "ax1.stairs(np.sum(hist_varied_ice_grad,0), energy_binedges, edgecolor='cornflowerblue')\n",
    "ax1.set_xlabel('muon energy / GeV'); ax1.set_xscale('log'); ax1.set_xlim(energy_binedges[0], energy_binedges[-1])\n",
    "ax1.set_ylabel('counts'); ax1.set_yscale('log')\n",
    "\n",
    "# Plot the histogram\n",
    "ax2.stairs(np.sum(hist_MC,1), zenith_binedges, edgecolor='blue', label='MC (unfitted)')\n",
    "ax2.stairs(np.sum(hist_varied_ice_grad,1), zenith_binedges, edgecolor='cornflowerblue', label=r'$+2\\sigma$ ice_grad_0 perturbation')\n",
    "ax2.set_xlabel('cos(Zenith)'); ax2.set_xlim(zenith_binedges[0], zenith_binedges[-1])\n",
    "ax2.set_ylabel('counts');\n",
    "\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('systematics_example1.png', bbox_inches='tight',dpi=400)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9ca6cc-a472-4975-ac1e-995833bca8a1",
   "metadata": {},
   "source": [
    "Example: `domeff`, for the conventional component only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "521a1c18-6433-4585-861c-f8ec74023bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "NuMu_totalWeight_conv = combined_MC_NuMu[:,5] * NuMu_flux_conv  * 239673600 \n",
    "NuMuBar_totalWeight_conv = combined_MC_NuMuBar[:,5] * NuMuBar_flux_conv  * 239673600\n",
    "\n",
    "totalWeights_conv_ = np.concatenate((NuMu_totalWeight_conv,NuMuBar_totalWeight_conv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9cee6350-0ea0-486a-87d2-d9d21a4da0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAJGRkQgPD/fIOfPs27cPAwcOlKSzMhgM2LZtG7Zt24bZs2dj3LhxHh1TWblw4QIGDBiAU6dOSY7fvHkTe/fuxd69e7Fw4UJs3LjRZRqu7777DsOGDYPRaLQfS0xMxOrVq/HDDz/gyy+/LPZ42rdvj127diEuLg5GoxEqlap0b4yIfNakVelI11mh1cgw9WktAECtEuwbYRdcIQEAMgH2Mo2f49z/IH/B5SbaKkV+mUqRX6d2bSAx8Y7fSrElJgKRkZ47351iwIKISiRvs3BuGk5EREREVDW0bt0a9957L06cOIGVK1di5syZkvK8lRdRUVFo3bp1kQGLU6dO4caNGwCALl26eGxfhr179wIAoqOjPXK+PMnJyYiJiYFcLsfMmTPRuXNnqFQq7Nu3D++//z7S09Px9ttv4+GHH5YEgo4fPw4AeOedd7B+/XqEh4dj69atJTr3wIED0bZtWyxcuBCff/65pN+CIiIi8J///AePP/54ic6XnJyMTp064dq1awgKCsKoUaPQq1cv1KxZExkZGdi2bRvmz5+Pc+fO4f/+7/9w5MgRhIRI95I8ePAghg4dCrPZDD8/P7z22mvo27cv/Pz8cPDgQUyfPh0vvvhisVfhtGvXDoAtIHTo0CF06tSpWO2IiPIYTCIMJttjnp4t1OjZQu20vlYjw+xhoS77e/GhIJdlDzTxwwNN/Eo/WB/FgAVVSuf3q2EwAmoVnGx8TURERERERCUxdOhQvPnmm/j2228xffp0yGS2WaSiKOLbb7+113GnYDDj/vvvL5/BFnL9+nVcuHABgC344klnz55FvXr1sH//fkRERNiPR0dHIzo6Gl27doXZbMaiRYswf/58e/m9994LAPaNo5VKpf1YcWm1Wmi1WoSFhTn0W1hYWBjCwsJKdL5Ro0bh2rVriIyMxK5du3D33XdLyrt3747BgwejS5cuuHjxIubMmYOpU6dK6owZMwZmsxlKpRLbtm1D165d7WXt2rXDoEGD0L59+yKDYAW1adPG/vyPP/5gwIKISqymVg5/lYjgAGYOqagYsKBK6fzeACQl2fKwkWftXBCa/2f/lrdHQ0RERES+pm1bICXF26Mof7VqAYcPe+58zzzzDN5++20kJiZi9+7d6NGjBwDb/glXrlyBTCbDM88847af1NRU+/OaNWuW23gLSiyQV6PgzXtP+fTTTyXBijydO3fGAw88gAMHDthXgFQWJ06cwIYNGwAACxYscAhW5GndujXGjBmDWbNmYenSpZKARXx8PP78808AwOjRoyXBijwRERH46KOP8OSTTxZrXAX/TSV6Mp8KEVUZ4x71zMo/Kj0GLIioRAJrmHGXDAi8C+BHCBERERF5WkoKkJTk7VFUPREREejRowe2b9+OlStX2gMWeemgunfvjjp16rjtJysry/5co9GUz2ALyUtBBQChoa7TdpQHrVaLfv36uSxv06YNDhw4gIsXL3pwVHdu/fr1AICAgIAi3x8AdO3aFbNmzcLVq1dx5coVRN5OlP7777/b6wwfPtxl+5iYGGi1WqSnp7sdl5+fH/z9/ZGTkyP5eycioqqDdxuJqESin8yCOliEIVMA4NkvA0REREREtWp5ewSe4Y33+dxzz2H79u1Yu3YtPvvsMwDAjz/+CKB46aAAICgoP5e3Tqcr+0E6cevWLftzTwcsGjdubE+f5Uy1atUASAM5lcHh28t79Ho9FIri3zpKSUmxByzy9tNQqVS47777XLZRKpVo3bq1feN0d0JDQ5GTkyPZ5JyIiKoOBiyoUqpWzwghBAjVAoDKy6MhIiIiIiJP8WSaJF8zaNAgvPTSS8jKysL69eshiiIyMzPh7++Pxx57rFh9VK9e3f782rVr5TVUCbU6f6PUnJwcj5wzT0BAQJHlecEMq9XqieGUmevXr5eqnV6vtz9PS0sDYAvauAt6lCR9WN7fsb+/fylGSES+zpupJZOTvXPeyoYBC6qU2g3JhiADRCsAVPP2cHyKLl0OXaYIWLk5ERERERFRVRIYGIiYmBh8++23WLlyJURRBAAMHDhQsnKiKC1btrQ/P3LkSLmMs7AaNWrYnxdcbUGlZ7FYAAANGjTAL7/8Uux2DRo0sD/P+/cjCO6/O+bVdcdqtSIjIwOA9O+diKi4gu7Rw6+hCGOOgMO/Fh10Ju9gwMIHREVFSV6bTCYvjYSqAo3WUiAlFBERERERVSXPPfccvv32W2zbts1+rLjpoACgefPmqF69OlJTU7F3715kZmYiOLh8NzgteOM6b1a/KwXTN1mtVpfpnDyVzqqiuuuuuwDYVsk0bdq0RGmh8uSlw7p58yYsFgvkcrnLusVd0ZGRkWFfrcKABRGVRp0WuVAHi9ClCUg+4tmARcfhGfDTWHFxWzCA/M/Eg2dzsTZO77rhbSEBMrwzOERybOUuHY5dMhbZLutWemmG6zUMWFClZDEDCpXtkbxnzvpMXEu3QK0UMPVpraRsx3EDNh+xLdWN7RGIqLpKe1m6zoppa22zYu6rp8LQ7tLN+L7YmoULKba/3OnPaqGU5wdHCn6IP94hAA808bOXmSwiJnyTDgBoWEuBFx+SzgLL+xB39gFPRERERETAgw8+iNq1ayP5dt6KmjVrok+fPsVuLwgCYmNjMWfOHOh0OixevBj/+9//ymu4AICGDRsiICAAer0eZ8+eLbJuwZUiaWlp9hvzhf3zzz9lOsbyVJwVDCWt27p1a2zatAl6vR779+9Ht27dSjyuFi1a4LvvvoPRaMTRo0dx//33O61nNpvx999/F6vPgn+/LVq0KPGYiIjyyOVAYqJnzzl+hRXpOhGfL7WgYMDCaBaRrivOSjPH9IK6XKvbtjp98VaxVRSud4aiKuPkyZOSnx07dnh7SHfMbBAkj+QdmXrbB2q6zvED02DMKxNhskg/GK0i7GW6XMe2WTn5bQvL+xBP14kwmh3L88qychzLLt0wI10nIkNfufLHEhERERF5ilwux9ChQ+Hn5wc/Pz88++yzRc6Md+bVV1+17+0wefJknDlzpljtrFYrvvnmmxKPWaFQoH379gCAQ4cOFVm3YMqiw0VsiLJ69eoSj8NbCu7hkZubW6y67uo9+uij9uezZs0q1bh69eplf75ixQqX9datW+d2ZUyegn+/Xbp0KdW4iMi3HVoThHUzg3FoTfFSHZaHL7ZmS16rFAK0Gvc/IQGOt/I1fjK37YIDKtf9U66wIKJSCw4QkGMUoFY6fvCpVbYPRQCSFRIAIBNgL9P4OX7YBvnnty0s70M873lheWVB/o5ll25Yino7REREREQE4MMPP8SHH35Y6vYRERFYsGABnn/+eeh0OnTr1g3ff/99kbP0T506hVdeeQU3btzAs88+W+JzdunSBTt27MCxY8eQm5sLPz8/p/U6deoEhUIBs9mMjz/+GH369HFYdTBz5swigxkVTe3ate3PL1y4gObNm7ute/36dWRlZbncmyQ6Ohp9+vTBtm3bsGnTJrz77rt47733XPabkJCAuLg4DBkyxH6sXbt2uP/++3HkyBF8/vnnGDx4MDp37ixpl5ycjHHjxhXrfQJAfHw8AKB+/fqoU6dOsdsREeXJvqHAzSRA7cW5rBq19PfOA038JBlESsKWtURTZJ3ERB2+LN/FjmWKAQsiKrVxj7rORduzhRo9W6idlmk1MsweFuqybeFUTgUV9SGulAtF9jt7mBZW0RYwISIiIiKi8jN8+HAkJiZi8uTJuH79Orp3744+ffrg0UcfRbNmzaDVanHr1i2cPXsWGzduxJYtW2CxWCSbdpdEv3798N5778FoNGLv3r2S2f0F1ahRA48//ji+++47bN26FQMGDMCYMWNQs2ZNXL58GStWrMC6devQoUMHxMXF3ckfgcd07NjR/vy1117DxIkTUbt2bXsgpn79+vY9KPLqWq1WvPjii3j55Zdx11132es2atTI3teyZcvQtm1bJCcn4/3338fWrVvx/PPPo0WLFlCr1bh58yaOHTuGLVu2YMeOHRg4cKAkYAEACxcuROfOnWEymdC7d2+89tpr6Nu3L/z8/HDw4EFMnz4dqampaNmyJY4ePVrk+xRFETt37gRg+/smIqKqiQELIioViwWofBNabKs5atUCKtGEKSIiIiKiSmnSpEmIiorC66+/joSEBGzbtk2ymXdhUVFRpU4/FB0djaZNm+LMmTNYtWqVy4AFAMybNw9//vknzp07hw0bNmDDhg2S8ieeeAKjRo0qso+KpFGjRnjiiSfw/fffO/0z/vfff1G/fn0AQM+ePdG+fXscOHAAq1atwqpVqyR1RTE/tW54eDji4uIwePBgHDp0CAcPHsTBgwddjsPZ5uoPPPAAvv76a8TGxsJgMGDGjBmYMWOGvVyhUODzzz/Hvn373AYs9uzZg8TbCedLswqHiIgqBwYsqFJS+ouSR/KOpCRvj4CIiIiIiCqyQYMG4ZFHHsHatWuxefNmHDp0yJ6OKDg4GPXr10f79u3x2GOPoUePHiXaQLqw//znP/jvf/+LH3/8EQsXLpTs7VBQzZo1cfDgQXz44Yf46aefcPnyZWg0Gtx7770YOXIknn32WezatavU4/CGb775Bm3btsXatWvxzz//ICsrC1arY74TmUyGbdu2YdasWfj1119x4cIF6HQ6SaCioHr16uHgwYNYv3491qxZg4MHD+LatWswmUzQarVo3LgxOnTogAEDBrjcU2LIkCFo2bIlZs6cie3btyM1NRU1atRAp06d8L///Q8PPPAA9u3b5/Y95gVXWrdubd+zhIiopIJrmWFSiQiuJoC3xismQXT1W4mqrMTERERGRgIArly5UinzPr6w4BYEGSBagcVjq3l7OD5l2Ow0qDQiDJkCdi5wnX6pIkpOBqxWICICuD0xh4iIqMKoCtdo5Nvu5N/wuXPnYDaboVAo0Lhx4/IaIlVx2dnZqF+/Pm7evImVK1dyFn4VkpWVhbp16yI9PR3ffvstnn766VL1w88aIhrx6S3I5LZ7iqFBroPkTcKVGNk7UHJszvpMXEt3vz9q/7b+6BqVHzRP11kxbW0GMvQiRNG2/2pRKc3LWmX7nsEwEhGVSFgYkK4DatWufDf9W3UxIS1DRGiIAEDp7eEQEREREVEZCgwMxBtvvIE333wT06ZNw9NPPw2ZTObtYVEZWLBgAdLT09GsWTM89dRT3h4OEVVieQv5BBmQrnM9jz/b4LhKLVMvFtkmj8EkrWMViz4XSTFgQZXSjk+1uHYNqFkTwFhvj8a3NAlXIttgRaA6/8L/TiPMxfH6gGDUCpXbXx88m4u1cXq37UICZHhncAgAoEW/bKiDbatDgMq1OoSIiIiIiNz773//i4ULF+LMmTP4/vvveXO7CtDpdJg7dy4AYNasWQxCEdEduXVZAUWAFSFhVmg1rldYFLzvlSc4QECO0X3qQrVSWkcmQHKukAB+jhWFAQuqlIw6GfQZgDHQfV0qW4WXwwGeiTBbCmWvM5qLd07AMSJORERERERVk1qtxjfffIPff/8dZrPZ28OhMnDp0iWMGTMG1apVwyOPPOLt4RBRJRe/KhhJSaVLFz7u0eBSnVOrkXk0BVRlx4AFEd2xsoowF0VeaPM9lUIoVtuCUWt9hhz6LCtgZSSbiIiIiKiq6ty5Mzp37uztYVAZad68OaZMmeLtYRARkYcwYEFEd8wbEeYHmvjhgSZ+JWoTEGK5nRKKeQOJiIiIiIiIiIgqGgYsqFJq8Ug2mllFKGQCAOaFIiIiIiIiIiIiIqrsGLCgSiniXiMEGSByewIiIiIiIiIiIiIqhofeuAWZArCaAaCat4dDTjCROxERERERERERERFVeYJM+kgVD/9qqFIy5QqSR6LiUGlEySMREREREREREfkOUZQ+UsXDgAVVSlaT9JGoOARB+khERERERERERL7DqBMkj1TxcA8LHxAVFSV5bTLxLj/5JqsVkMtsj0RERERERERERFSxcIUFEfkMk16QPBIREVHlc+TIEUyfPh0PP/wwIiMj4efnh8DAQDRp0gSxsbHYu3dvifrbsmULBg0ahDp16sDPzw916tTBoEGDsGXLlmL3odfrMXv2bLRr1w7VqlVDYGAgmjVrhnHjxuHy5cvF7ufkyZN48cUX0ahRI/j7+6NGjRro2rUrvvzyS5jN5mL389133+Ghhx5C7dq1oVarUb9+fQwdOhQHDhwodh9ERERERN7AFRY+4OTJk5LXiYmJiIyM9NJoiIiIiIhKp1u3btizZ4/DcaPRiHPnzuHcuXNYsWIFhg4disWLF0OlUrnsSxRFvPjii1i0aJHkeFJSEtatW4d169Zh1KhR+OKLLyAUkU/ywoUL6NevH/755x/J8TNnzuDMmTNYvHgxVq1ahb59+xb53pYsWYIxY8YgNzfXfsxgMGDv3r3Yu3cvli9fjg0bNuCuu+5y2YfBYMDgwYOxYcMGyfFLly7h0qVLWLVqFaZMmYJJkyYVORYiIiIiIm/hCguqlPwCRckjUXFcPeWHf/b74eopP28PhYiIiEohKSkJABAeHo5XXnkFa9euRXx8POLi4jB37lxEREQAAFauXInY2Ngi+3rnnXfswYrWrVtj9erViI+Px+rVq9G6dWsAwKJFi4q8uZ+dnY1HHnnEHqwYOXIktm/fjj/++APTpk1DYGAgMjIyMHjwYBw7dsxlP1u3bsWoUaOQm5uLmjVr4pNPPsHBgwexefNmDBo0CABw4MABDBo0CNYicluOGDHCHqzo0aMHfv75Z8THx2PJkiVo2LAhrFYrJk+ejMWLFxf5Z0NERERUVclVouSRKh5BFLknuq8puMLiypUrqFOnjpdHVHIvLLgFQQaIVmDx2GreHg5VEnXqAElJQEQEkJjo7dEQERFJVYVrtPL2yCOP4LnnnsNjjz0GuVzuUJ6amopOnTrh7NmzAIA9e/agS5cuDvXOnz+PZs2awWw2o23bttizZw/8/f3t5Xq9Ht26dcPhw4ehUChw5swZNGzY0KGfKVOm4L333gMAzJo1C+PHj5eUx8XFoWvXrjCbzejRowd27Njh0IfZbEazZs1w/vx5BAcH48iRIw7nGjNmDBYuXAgAWLFiBZ577jmHfnbv3o3u3bsDAPr3749169ZJ/oxSU1PRpk0bXL58GaGhobh48SK0Wq1DP3fiTv4Nnzt3DmazGQqFAo0bNy7TcRER5eFnDRH54j3FyvY9gyssqFIy6gUYDbZHouJq3E2Pjk/o0Lib3ttDISIiolLYsGEDnnjiCafBCgCoXr06PvroI/vrtWvXOq338ccf2/eE+PTTTyXBCgAICAjAp59+CsAWUJg3b55DHyaTCfPnzwcANGvWDK+//rpDnQ4dOmDEiBEAgJ07d+LPP/90qLNu3TqcP38eAPD22287DYzMnj0boaGh9ufOzJo1CwAgl8uxcOFChz+j6tWr48MPPwQApKWlYcmSJU77ISIiIvKkr37LxvgVaRi/Ig0Go3Re/ZELRnvZ/jO5Dm3zyhZsynIoW7VXZy+/kWkpt/FT2WPAgiqlHZ+E4uvXq2HHJ6HeHgpVInVa5KJ5t1zUaeH4S46IiIiqhrxVBoBtf4nCRFHE+vXrAQBNmzZF+/btnfbTvn173HPPPQCAn3/+GYUXpu/atQvp6ekAgGHDhkEmc/7VqmBqqp9++smh/Oeff3Zat6CAgAA88cQTAIATJ07g3LlzkvLs7Gxs374dANC7d2+Xs+YGDRqE4OBgl2MhIiIi8rRsgxXpOhHpOhGF0wAZzaK9zGhyTBKUV5aV45gyU2/Ib1vwMu70dn8c+kWN09v9HdpQxcCABRERERERVRlGo9H+3FkQ4d9//7XvhdGtW7ci+8orT0xMREJCgqRs7969DvWcadu2LTQaDQBg3759DuV5/dxzzz2oVauW27E46yc+Pt6+WXdRY1GpVPYATXx8PEwmk8u6RERERJ4QqJZBqxGg1QgonEdFpRDsZSqlY5aVvLIgf8drvgB1fluhQNNLh/xxdGsALh1iwKKiUnh7AEREnmK1CjDlirBamUqMiIioqtq9e7f9edOmTR3KT58+XWR5QQXLT58+jQYNGpS4H4VCgYYNG+LYsWOSNoBtZUTi7Y21SjqWgkr6nrZt2waz2Yxz586hefPmRdYn37Z8+XIMHz4cgC3YV79+fe8OqALZv38/5syZg7i4OKSmpsJisaUbSUtLk+wPs3LlSnz11Vc4fvw4MjIyIIoiWrZsib///ts7AyciqmBG9g50WXZ/QxXub6hyWT57mOvMK0930eDpLpo7Ght5BwMWROQzZDIRSj/Akuu4jJCIiIgqP6vVipkzZ9pf56VRKujKlSv25+42HMzbnLBwu4KvNRqN282rIyMjcezYMdy4cQO5ubnw8/MDYFu5kZdqqizGUpp+ShKwyAuuuJKcnFzsvogqs19//RUxMTH2IIUrb7zxhst9Z8qS2WzG8ePHER8fj0OHDiE+Ph6nTp2yj68sg01HjhzBli1bsHfvXpw4cQLXr1+HUqlEeHg4OnbsiBEjRqBLly7F6uvff//FJ598gt9++w2XLl2C1WpFREQEevfujf/85z+IiooqkzETUcWzbEc2dAYRGrWA4T1dByzINzFgQZVSt/+kQ6YQYTULALTeHg4RERERVQAff/wx4uPjAQAxMTFo27atQ52srPxNGQMDi/6CnJfKCbCthnDWj7s+nPWTF7Ao67HcaT/uFAx2EPmy119/HRaLBeHh4Zg5cyaioqKgUtlmAOftE3PlyhXMnTsXgG1PnHfeeQeRkZGQyWTw9y/bNCTTpk3DlClTyrRPZ7p164Y9e/Y4HDcajTh37hzOnTuHFStWYOjQoVi8eLH9z8SZRYsW4eWXX5ak8QNg72fx4sWYN28eXnrppTJ/H0TkfaeumJCuE6HVeD4DRrW6JiBYRDWtAEDp8fOTewxYUKXkH2yFIANEK2fKExEREZEtFdRbb70FAAgLC8Pnn3/utJ7BYLA/L+pmGgB7YAEAcnJynPbjro+i+inrsdxpP0Tk3uXLl+0b30+YMAFDhw51Wm/nzp32FQ6LFy8u19UCYoHdZNVqNVq1aoUbN27gwoULZXqevP1/wsPDMXjwYHTp0gV169aFxWJBXFwcPvroIyQlJWHlypUwm81YtWqV036+++47jB49GgAQEhKC119/HT179oSfnx/++usvzJo1C+fPn8eYMWNQo0YNPP7442X6PojIt7UckA11sAhDpgDAdUop8h4GLIjIZwhy6SMRERFVDSdPnkRMTAzMZjP8/Pzw/fffo2bNmk7rqtVq+/PCM3sLy9vIGoDDjOi8ftz1UVQ/ZT2WO+3HncKpqApLTk5Gu3btStQnUWWTd9MeAJo0aXLH9cpChw4d8MUXXyA6Ohr33XcfFAoFYmNjyzxg0bRpU0yfPh2PPfYY5HLpl6r27dtj6NCh6NSpE86ePYvVq1fjpZdeckgPpdfr8corrwCwrQjbt28f7r33Xnt527Zt8eSTT6Jz5844fvw4Xn75ZTz88MOS1WFEVPm9+2QIRBGSzbCJ8jBg4eM++CEdQdWc/+Jv19gPgzsGSI5NWpUOg8n9qoZnu2nQsn7+7K5L181YsDmriBb5pg7RQq3K/8Ta9ncOfjtqcFrXqOcnGxWfyl+UPBIREVHl9++//6JPnz5IS0uDXC7H6tWr0a1bN5f1g4KC7M/dpUTS6XT254VTLeX1U5y0Sq76Keux3Gk/7rjbH4PIFxQM+imVrlOJFLdeWXjooYfKtf88GzZsKLK8evXq+Oijj9C/f38AwNq1ax0CFps3b8b169cBAK+88ookWJEnODgYc+fORe/evZGSkoLly5djzJgxZfQuiKgi6N5ZhpQU75y7KzPNVXgybw+AvCtTLyJd5/xH72Rj4nSd1WX9gj8ms7Sd2eq+Td5P4bMajI51hNv/ckVr+fy5EBEREVHFd/XqVfTq1QtXr16FIAhYunQpYmJiimxT8Ka7u02kC64oKLx/Q14/Op0O6enpxeqnRo0akpRMZT2WO+2HfE9aWhreeustNG3aFP7+/ggLC0OvXr3www8/lKifhIQEvPbaa4iKikJQUBACAgLQuHFjjB49GsePHy+yrSAIEATBvgfDzp07MXDgQISHh8Pf3x/NmjXD1KlTJcE2ANi0aRP69u1rr9e8eXPMmDGjWKuezGYzlixZYm/v5+eH6tWro2vXrpg3b54kzVqe2NhYCIKAHj162I/16NHDPn5BELB8+XLUr18fgiDgvffec3iPeT8JCQlux1hZde/e3f7c2QqPQ4cO2Z8//PDDRfaTt3ps7dq1ZTdAIqoQUlKApCTv/FDFxxUWPk604nbONkdfLxMwsVA6zi6jZVAo3c9OH/kCcON8/uvgWgLaPF681RBNmwKWAteY9dsJaNBO2vZ2KlCYDYy5UfGZTYDSz/ZIRERElVtqaip69+6NixcvAgA+/fRTPPfcc27bNW/e3P78zJkzRdYtWN6sWTOHfn788Ud7vfbt2zvtw2w222/aFe4jMDAQkZGRuHLlyh2PxVm9ovpRKBRo1KhRkXWpajt16hR69eqF5ORk+zGDwYDt27dj+/bteP755x1mxzvz9ddfY9SoUZIVBQBw/vx5nD9/HkuWLMHUqVPx9ttvu+1r5syZmDBhgmRPhjNnzmDy5MnYsmULtm7dCo1Gg9deew3z58+XtD19+jQmTJiAPXv2YMOGDQ4pi/JcuHABAwYMwKlTpyTHb968ib1792Lv3r1YuHAhNm7ciMaNG7sd852YMmWKPbCxbNkyxMbGluv5PKFgwEgmc/y+fuvWLftzV6n7ANtnVLVq1XD16lX88ccfMJvNUCh4C4uoqpHJgNq1PXvOvF8PMqYLr7D4ae/jzv/ph8Pri7/BzHeTtaU6T1KSAqf/LN1GNknr/LF/nfP8uhERAKaWqlvyQZZcAUo/EZZcphIjIiKqzDIyMvDQQw/ZbzjOnDmz2OlCGjRogPDwcFy9ehW7d+8usu6ePXsAABEREahfv76krHPnzvbnu3fvdhmwOHz4sH1meKdOnRzKO3fujNWrV+Off/5BSkoKatWq5bSfgmMt3E90dDRUKhWMRqNk8/HCjEYjDhw4IGlDvinv/1BesOLJJ5/EsGHDEBYWhrNnz2Lu3LlYunSp29URGzduRGxsLERRRGBgIF5//XX06tULCoUCf/zxB2bMmIHU1FRMmDABWq0WL73kOg/H5s2bER8fjw4dOuDll19GkyZNkJqaivnz52Pz5s34448/MHPmTFSrVg3z58/Hww8/jBdeeAH169dHYmIiZsyYgQMHDmDLli346quv8OKLLzqcIzk5GZ06dcK1a9cQFBSEUaNGoVevXqhZsyYyMjKwbds2zJ8/H+fOncP//d//4ciRIwgJCQEATJs2DePGjcOhQ4fw/PPPAwCWLl2K6Ohoe/916tRBx44dYTQasXDhQnz++ecA4PDnGBERUYy/pcqp4GdV06ZNHcoL7kWRkZHhsh9RFJGZmQnA9tl1/vx5p/0RUeVUo5ERqjAgJBj4a5dnr0fGrwDSdUBYmEdPSyXAgIWP+/OXAFTGa6VqdU2QyUVoQwQA5ZsPlIiIiIgqDr1ej379+uHIkSMAgIkTJ+LNN98sdntBEPDoo4/i888/x5kzZ3DgwAGnwYYDBw7YVyM8+uijEArtCtm9e3eEhIQgIyMDK1aswBtvvOFQBwCWL19uf+4sXdXAgQOxevVqe11nwQa9Xo/vv/8egG01ReENfIOCgvDggw9i8+bN+P3335GYmOh0v4mffvrJfgPQXeosqtref/99e/qw6dOnS1Y/tGnTBo8//jgeeeQRbNu2zWUfJpMJo0ePtgcr9u7di1atWtnL27dvj8ceewwdOnRAcnIyxo0bh8GDB6N69epO+4uPj8djjz2GNWvWSFZH9OrVC507d8aBAwfwySefwGQy4dVXX8XHH39sr3P//fejV69eaN68OS5duoTPP//cacBi1KhRuHbtGiIjI7Fr1y7cfffdkvLu3btj8ODB6NKlCy5evIg5c+Zg6lTbDLmIiAhEREQgNTXVXr9BgwYOezBotVoAQFiBO2HO9mmoiqxWK2bOnGl//cQTTzjUKbhCbPfu3WjTpo3Tvv766y/JnjyXL19mwIKoCrn3/3RQB4u3s75wAgVJMZ+Oj6tZE0hMrHw/D43ORvSQbEQPdr/JIVGeE1s02L44ECe2ON9onoiIiCo2o9GImJgY7N+/H4Btw9YPPvigxP28+uqr9tQiL7/8MnJyciTlOTk5ePnllwHY0pK8+uqrDn2oVCr897//BWBLRTNnzhyHOnFxcViyZAkAoFu3bpKZ2HliYmLQsGFDAMCMGTOc5nwfP3480tLS7M+dGTduHABbCqoxY8bAkpdD9bbU1FR7YEer1eKFF15w2g9Vfbm5uVi2bBkA4L777nMa8FMqlViyZEmRm0WvW7cOSbeTgU+cOFESrMhTr149zJ49G4At8JZ3XmcCAgKwaNEih1ROcrkco0ePBgBkZWWhRo0amDVrltP2w4YNAwAcO3bMYfb+iRMn7JtGL1iwwCFYkad169b2FVtLly51OV5y9PHHHyM+Ph6A7bOtbdu2DnX69u1r/3c1d+5cSQAoj9VqxcSJEyXHsrKyymHERERUETFgQUQ+48Z5Ff79S4Ub5xm9JyIiqoyGDBlin/Hds2dPjBgxAidOnHD5c/bsWaf9NGnSxH6D//Dhw+jUqRPWrFmDw4cPY82aNejUqRMOHz4MwBYgcJXHfvz48fbVDm+88QZGjx6NnTt34sCBA5gxYwb69OkDs9kMf39/zJs3z2kfSqUSn3zyCWQyGTIzM9GpUycsWLAA8fHx2Lp1Kx5//HEsXLgQgC191NChQ53207NnTzz11FMAgF9++QW9e/fGL7/8gsOHD2PZsmVo3749Ll++DMCWQis0tHTpWiuKbX/nYPyKNIxfkYZ/kqQblN3ItNjLVu3VObRdsCnLXl7Y/jO59rIjF6SbNxuMor3sq98cJ04t25FtL882WCVlRxOM9rI9Jx03dH77m3SMX5GGeb+W/03ZP//80x4AGzZsmNN9BgBbeqM+ffq47Of3338HYFu1lJciyZnBgwfb0yrltXGmd+/eqFatmtOy++67z/580KBBLgMpLVu2tD//999/JWXr168HYAts9OvXz+U4AKBr164AgKtXr0o2qi9rU6ZMgSiKEEWx0u9fUTAdXVhYmD0dVmF16tSxpwZLSkpCp06dsH79emRmZsJgMODAgQPo27cvtmzZIklbVziwTESV2/l9/jjwoz/O73OeAp58G1NCkcTKXTocu2S7MJ/4eAi0mvyL15OXTVi+03Zh/vD9/ujZQi1pO2lVOgwmETW1cox7NFhStu6AHn/8Y9uE7ZVHglDnrvx/epeum7Fgs+3CvEtzNQZESz+sPvwpE6lZFmj8ZJjylO1Ct3dLNQxGEWoV9yKg4ntgaCa04ebbGytJvwxt+zsHvx21fXl8oVcg7onI/xJ0I9OCWets6RNa363C012kKzQWbMrCpRtmAMDsYdIv//vP5OLng3oAwJDOGtzfMP+i22AUMWl1OgCgSbgSI3sHStou25GNU1dsX8DffTIEgWrGmImIyLf99NNP9uc7duyQ3MR0pl69ekhISHBaNm3aNFy/fh1Lly7FX3/9Zb/ZX9CIESOKXMERFBSEjRs3om/fvjh37hwWLVqERYsWSeoEBwfj22+/dTr7PE/fvn3xxRdfYOzYsbh27Zp9dUdB7dq1w7p161xuJAzYZoNnZmZi06ZN2LlzJ3bu3Ckpl8lkmDRpkn22emVmMIpI19k2ZjZZREmZKMJepjeIDm2zcqz28sKMpvx+jeZC/SK/38IBCQDQGfLbioW6N5nz2xpMjudOz7bCbAWC/R37LWsF91NwtuqnoHbt2mHjxo1Oy06cOAEAqF+/viT9UWEqlQqtW7fGrl277G2cKZzqrKC8NEslqVd4Rn5eEFKv15do8+aUlBRERkYWu74vOnnyJGJiYmA2m+Hn54fvv/++yA21Z8+ejX///Re//vorzp49i4EDBzrUufvuuzFo0CD76rWgoKDyGj4RecGVv9VISkKlTFNP5Y8BCx8XPSRT8lqXm3/xbi18kW3JvwA3GJ1cZOusMJgAf5Vjmb7AFwpLoWtwszW/LMfoeIGeobeNyVygYZ9WjMBSyQWEWG4HKxxVti+9REREdGdkMhmWLFmCxx57DIsWLcKhQ4eQmpqK6tWrIzo6GqNHj8bDDz/stp9GjRrhr7/+wmeffYYffvgB58+fh9FoRGRkJPr27YtXXnkF9erVc9vPyJEj0aFDB3zyySfYvn07rl69Co1Gg2bNmuGZZ57BCy+84PYmq7+/PzZu3IhVq1Zh+fLlOHr0KNLT01GzZk106dIFY8eORYcOHYr9Z1SRqVUCtBrb5CWlXDqJSRBgLwtQO05wCvKXQatxHhhQKfP7VSkK9Yv8fp1NJNGo89sW3s5Eqchvq1Y6jkkbKIPZIiLIv/wnqOStrgBQZKABQJE3nW/duuW2Tp68zeTz2jgTEBDgsqzgKpDi1iucFu369etux+mMXq8vVTtf8e+//6JPnz5IS0uDXC7H6tWr0a1btyLbqFQqrF+/HitWrMCCBQvw119/wWq1/Z/UarUYOnQopk6dat8/BEClXxVGRBVH4YmmVPEwYOHjArTSiziNn8x+IS0rfJEtz78Ad7ayQauRwWASERzgWBZQ4AuFvNA1uEKWX+avcrxADwmQwWQRofHj7HK6M6ZcAWKmCIvZ8d9SZfvSS0RE5IvEcojg9+3bF3379r2jPjQaDd544w288cYbd9TPvffe67BCozSefvppPP3003fcT0XWp5W/y0lMNYLlRd6MGNvX9UztTk390Kmpn9MytUoost/hPQNdlrWsr0LL+q7Tks54VuuyrKwV/H/kbKN4V3VdcddHcfspb3kBjAYNGuCXX34pdrsGDRqU15AqvatXr6JXr164evUqBEHA0qVLERMTU6y2giAgNjYWsbGxyM7OxrVr16BSqRAeHm5fSXbs2DF7/ebNm5fLeyCiimPPSQN+Pew+/ZuzzC5f/ZaNs1dNLlrkc5bZhSoeBix8XOFry6HdNQCcb0gcVVdZ5AX61Ke1Lsti2gcgpr3zmTD1whRF9vvmoGCXZUQlofQToQ4GDJmOX5gqw5feowlGmMy2GXpFfeElIiIiInKl4D4R165dKzLFUlGrEvL6SUlJcXvOa9euOZzb0+666y77WJo2bVqitFDkKDU1Fb1798bFixcBAJ9++imee+65UvUVGBiIwEDpdx+j0WjfwPvuu+9G9erV72zARFSh+GmsCNACfhogb4tlQ4EMFUVxltkl2+A680VBzjK7UMXD39A+ICoqSvLaZMqPOObmcNo2+R6LBahTx9ujKLkeY3VQB4sw6gSsGM+ABRERERGVXIsWLezPDx06hC5durise+jQIZdl9957L+Li4pCQkIDr16+7TC9lMpnw119/2dt4S+vWrbFp0ybo9Xrs37/fbdoici0jIwMPPfQQTp06BQCYOXMmxowZU6bn2LRpEzIyMgAATzzxRJn2TUTe1/3ldMhkgC0bnC2YrS6QoaIozjK7BKplxWrrLLMLVTwMWPg676/MJfKKpCRvj6Dk8tLwWi1F1yMiIiIicqVNmzYIDQ1FWloaVq5ciddee81pWqekpCRs27bNZT+9evXCV199BVEUsXTpUrz11ltO661du9Z+47lXr15l8yZK4dFHH8W0adMAALNmzWLAopT0ej369euHI0eOAAAmTpyIN998s0zPYTab8e677wIAlEolRo4cWab9E5H3CYUeAaBrlBpdo9Sl6m9kb9dpGanyYcDCB5w8eVLyOjExEZGRkV4aDZH35G24LZcDERHeHUtp/L3FH3KViEB/rowiIiIiotLx8/PD8OHDMXfuXPz999+YPXu2w/4rZrMZI0eOhNFodNlPTEwMwsPDcfXqVUyfPh0PP/wwWrZsKalz5coVjBs3DoBts+zhw4eX/RsqpujoaPTp0wfbtm3Dpk2b8O677+K9995zWT8hIQFxcXEYMmRIuY1pypQp9jEsW7YMsbGx5Xau4qhfvz4uXboEwPm+I0ajETExMdi/fz8A4JVXXsEHH3xQ4vOkpqYiICDA6QbqRqMRL7zwgn3/ijfffBN33313ic9BRBWb1Wrb49bKDE3kBAMWROQzwsKAdB1QqzaQmOjt0ZRcnTpqJCVVzmALEREREVUckydPxvfff4/ExES8+eab+Pvvv/Hcc88hLCwMZ8+exdy5c3Ho0CFER0e7TAulVCqxaNEi9O/fH1lZWejcuTPGjx+PBx98EAqFAn/88Qdmzpxp3wdjzpw5Xt+HYNmyZWjbti2Sk5Px/vvvY+vWrXj++efRokULqNVq3Lx5E8eOHcOWLVuwY8cODBw4sFwDFmUlOzsba9eulRw7f/68/fnatWslf/atWrVCq1atSnyeIUOG2Ffd9OzZEyNGjMCJEydc1lepVE73SNm1axdGjhyJZ555Br169ULdunWh1+vx119/4YsvvrCnmurTpw8mTZpU4nESUcVn0guQB4sw6TkhkxwxYOHjZPwXQD4oQy9i/Io0AMDUIVqoVfm/ILf9nYPfjhrc9lGvhsJhs+0Fm7Jw6YbZbdveLdWSDb4NRhGTVqe7bddjLLB5YRBg5X9cIiIiIiq9kJAQbNmyBb169UJKSgpWr16N1atXS+oMHz4cXbt2LXJVRL9+/bBs2TKMHj0a2dnZePfdd+2pfPLI5XJMnToVL730Urm8l5IIDw9HXFwcBg8ejEOHDuHgwYM4ePCgy/rBwcEeHF3ppaamFvn3NH78eMnrd999t1QBi59++sn+fMeOHbjvvvuKrF+vXj0kJCQ4LUtPT8dnn32Gzz77zGl5bGwsPv/8c6hU3LuPiMjX8K6Xj1OquIkF+R5RBNJ1tn/7hf8HGIyivawo1QId1y1m5ViL1dZglNYRgWK1UwcDcoUIuF6ZT0RERERULFFRUTh58iQ+/PBDrFu3DpcvX0ZQUBBatGiBkSNHYsiQIVi+fLnbfoYNG4Zu3bph3rx52LZtGy5fvgyr1Yrw8HD07NkTL7/8smSjb2+rV68eDh48iPXr12PNmjU4ePAgrl27BpPJBK1Wi8aNG6NDhw4YMGBAkRuSU+l16dIFs2fPxo4dO3DmzBlcu3YNMpkM4eHh6NGjB2JjY9G+fXtvD5OIiLxEEJ0lJqQqreAeFk+9fwyrJ1Wci0ei8vTBDxnI0EsDDZVphUVKsm2FhdqqqJQprYiIqGgFr9GuXLmCOnXqeHlERCVzJ/+Gz507B7PZDIVCgcaNG5fXEInIx/GzhqhiGDozDepgEYZMASvfCvX2cKq8yvY9gyssfFziCT9vD4HIY94ZHOK2Tp9W/pJgQkkUDmAUl1olYPYw97+gn5mWjgGvZyI3WwZAW6pzERERERERERF5k8JPlDwSFSTz9gDIu87vCfD2EIiomGRyEXKl7ZGIiIiIiIiIqDKSK6WPRAVxhQURUSWRq5PBmCtCNAvuKxMREREREREREVUyDFgQEVUSfhqrPccjEREREREREVFZ+eEPPeLP5QIAXh8QjFqhcnvZhRQTvtiaDQDoca8afdtIU2nn7RkaEiBzSMe96c8c7Dxh2yv0xYcC0bCWEofXBCItQ0RoiACMLc93RZURAxY+LqCa+w2CiYiIiIiIiIiIqOrS54pI19lSUFtEaSpqswX2MoPRMU11ht56u9zqUGYw5vdrttiOpf6rQlISYIkowzdAVQYDFj6u3VNZ3h4CEREREREREREReVGAnwCtxpbRQS5IMzso5LCXqVWOWR9CAmQArLcfpdSq/H4VcodiIgcMWBARERERERERERH5sMEdAzC4Y4DTsoa1lJg9LNRl28JpoArq28bfIYUUUVEYsPBxVseVWkRUQSn8RMkjEREREREREVFpzfs1C1k5VgT5y/Bq/yCPnbd281wE1hMRpBEA+HnsvFQ5MGDh40wGbt5LVFnIldJHIiIiIiIiIqLSSrplRrpOhFbj2RnNTXvqoQ4WYchkwIIcOSYWIyIiIiIiIiIiIqIqTSEXoJDZHokqCq6wICKqJIx6AX6BIox6XkgQERERERER0Z35bZ4WKSm25yvf8tx5u77kuXNR5cOABRFRJSFapY9ERERERERERKWVkgIkJXl7FERSDFj4OG7eS1R5mE0CjDkizCausCAiIiIiIiKisiGTAbVre+58cvnt88o9d06qPBiw8HFyfjAQVRp7v9QiKQmIiAAwydujISIiIiIiIqKqoHZtIDHRc+cbvwJI1wFhYZ47J1UeDFgQEVUS2ggTrAGANhQAlN4eDhERERERERFVYpGtDAi9R0SgvwBA7e3hEAFgwMLnHfouiDO1iSqJ1jHZUAeLMGQKAEK9PRwiIiIiIiIiqsQadc4pcJ+BAQuqGGTeHgB5l+4WY1ZERERERERERERE5H28W01EVEno0+XQZYoQrNx0m4iIiIiIiIjuzIktGmRkAiHBAN7y3HlDAmQArLcfiaQYsCAiqiQCtJYCSzWJiIiIiIiIiErvxnkVkpKAiAjgaIIR3+zWuW2jVgqY+rRWcuyHP/SIP5frtu199VQY2l2DdwaHlHbI5AMYxvJxjTrrvT0EIiIiIiIiqoCWL18OQRAgCAISEhK8PZwKZf/+/YiJiUGtWrWgUCjsf07p6emSeitXrkTXrl0RGhoKmUwGQRDQqlUrr4yZiKgoJjOQrhOL8WN1aKvPLU47Ebpcx7ZEhXGFhY+rc5/76CcRERERERER2fz666+IiYmBxWIpst4bb7yB2bNnl/t4zGYzjh8/jvj4eBw6dAjx8fE4deqUfXz//vsv6tevXybnOnLkCLZs2YK9e/fixIkTuH79OpRKJcLDw9GxY0eMGDECXbp0KbKP5cuXY/jw4cU637JlyxAbG1sGIycid5QKQKtxn9FBrXSsE+AnFKutxo9z58k9BiyIiIiIiIiIiIrp9ddfh8ViQXh4OGbOnImoqCioVCoAQHBwMADgypUrmDt3LgCgffv2eOeddxAZGQmZTAZ/f/8yHc+0adMwZcqUMu3TmW7dumHPnj0Ox41GI86dO4dz585hxYoVGDp0KBYvXmz/MyGiiqtaPSOEECBUC7Ssr0LL+qX7fzu4YwAGdwwo28GRz2LAwseZTcyFT1RZqDSi5JGIiIiIiDzr8uXLOHfuHABgwoQJGDp0qNN6O3futK9wWLx4MaKiosptTKKY//1ArVajVatWuHHjBi5cuFCm50lKSgIAhIeHY/DgwejSpQvq1q0Li8WCuLg4fPTRR0hKSsLKlSthNpuxatUqt31u3boV4eHhLsvr1KlTZuMnqshuZFowa10mAKD13So83UUjKV+wKQuXbpgBALOHhUrK9p/Jxc8HbSnfh3TW4P6G+UEHg1HEpNXpAIAm4UqM7B0oafvAM9kAANEKANXK6u0Q3REGLHycxeTtERBRcQmC9JGIiIiIiDwr76Y9ADRp0uSO65WFDh064IsvvkB0dDTuu+8+KBQKxMbGlnnAomnTppg+fToee+wxyOVySVn79u0xdOhQdOrUCWfPnsXq1avx0ksvuU0P1aRJkzJLV0VUmYmibf8IANAbHCcpZuVY7eWFGU2ivcxoltYRkd9vtsFx/wirBZDJHQ4TeRUDFkRElYQoAsLtRyIiIiIi8rzc3Px9IJVK5R3XKwsPPfRQufafZ8OGDUWWV69eHR999BH69+8PAFi7dq3bgAUR2QhC/v4RAWrHWYpB/jJoNc43rFYp8/ePUCmkbQXk9xuodtw/Ii9gwfsMVJFwpxMiokrCqBMkj0REREREdyItLQ1vvfUWmjZtCn9/f4SFhaFXr1744YcfStRPQkICXnvtNURFRSEoKAgBAQFo3LgxRo8ejePHjxfZVhAECIJg34Nh586dGDhwIMLDw+Hv749mzZph6tSp0Ol0knabNm1C37597fWaN2+OGTNmwGg0uh2v2WzGkiVL7O39/PxQvXp1dO3aFfPmzYPBYHBoExsbC0EQ0KNHD/uxHj162McvCAKWL1+O+vXrQxAEvPfeew7vMe8nISHB7Rgrq+7du9ufl/UKD6KqrEawHLOHhWL2sFCHdFAAMLZvkL28sE5N/exlBdNBAYBaJdjLCqeDAgCzgfcZqOLhCgsfUDhXpsnEPFBERERERES+7NSpU+jVqxeSk5PtxwwGA7Zv347t27fj+eefL9bs+K+//hqjRo2SrCgAgPPnz+P8+fNYsmQJpk6dirffftttXzNnzsSECRMkezKcOXMGkydPxpYtW7B161ZoNBq89tprmD9/vqTt6dOnMWHCBOzZswcbNmxwSFmU58KFCxgwYABOnTolOX7z5k3s3bsXe/fuxcKFC7Fx40Y0btzY7ZjvxJQpU+yBjWXLliE2NrZcz+cJBQNGMhnnyBIRUckxYOHjVP5c80VUWVy/oIJZtEIh8MKfiIiIiEovIyMDDz30kD1Y8eSTT2LYsGEICwvD2bNnMXfuXCxdutTt6oiNGzciNjYWoigiMDAQr7/+Onr16gWFQoE//vgDM2bMQGpqKiZMmACtVouXXnrJZV+bN29GfHw8OnTogJdffhlNmjRBamoq5s+fj82bN+OPP/7AzJkzUa1aNcyfPx8PP/wwXnjhBdSvXx+JiYmYMWMGDhw4gC1btuCrr77Ciy++6HCO5ORkdOrUCdeuXUNQUBBGjRqFXr16oWbNmsjIyMC2bdswf/58nDt3Dv/3f/+HI0eOICQkBAAwbdo0jBs3DocOHcLzzz8PAFi6dCmio6Pt/depUwcdO3aE0WjEwoUL8fnnnwOAw59jREREMf6WKqfdu3fbnzdt2tRt/djYWJw+fRppaWkIDg5Go0aN0KtXL7z00ktV+s+JqKB/kkwwWUQo5QLuiSjfFHJElQEDFj7g5MmTkteJiYmIjIwEwM17iSqTk5s1SEoCeN1ORERERHfi/fffR2JiIgBg+vTpktUPbdq0weOPP45HHnkE27Ztc9mHyWTC6NGj7cGKvXv3olWrVvby9u3b47HHHkOHDh2QnJyMcePGYfDgwahevbrT/uLj4/HYY49hzZo1ktURvXr1QufOnXHgwAF88sknMJlMePXVV/Hxxx/b69x///3o1asXmjdvjkuXLuHzzz93GrAYNWoUrl27hsjISOzatQt33323pLx79+4YPHgwunTpgosXL2LOnDmYOnUqAFuQISIiAqmpqfb6DRo0wL333ivpQ6vVAgDCwsLsxwrXqaqsVitmzpxpf/3EE0+4bVMwwHHz5k3cvHkTBw8exEcffYR58+Zh9OjR5TJWoopk+qpsqDQiDJkCdi5wTPlUnrq6jiMTeQ0DFj7ObGTEgqiyuLtDDpoEWdCwrREXUoLQsFb+zIuUNAs++iUTANCusR8GdwyQtJ33axaSbpmhkAuY8axWUrbnpAG/Hs4BADzbTYOW9fNzXmYbrHhvTQYAoHmkEsN7SnNefvVbNs5etaWZmzpEC7Uq/zPlyAUjVu+z5Roe+EAAOjX1u5O3T0RERERlIDc3F8uWLQMA3HfffXjzzTcd6iiVSixZsgR33323y5TC69atQ1JSEgBg4sSJkmBFnnr16mH27Nl49tlnodfrsWzZMowfP95pfwEBAVi0aJFDKie5XI7Ro0fjwIEDyMrKQmRkJGbNmuW0/bBhw/D+++/j2LFjyMjIsK+OAIATJ07YN41esGCBQ7AiT+vWrTFmzBjMmjULS5cutQcsyL2PP/4Y8fHxAICYmBi0bdvWZd27774bgwYNQocOHewTKi9evIgff/wRa9euhcFgwIsvvghBEDBq1CiPjJ/IW6wW26PFAtz+WCXyaQxY+Lh9i7UAr7+IKoV6bQxQB9vSuJkt0jKLKCJdZyvT5zqmesvKsSJdJ0IhcywzmPLbmszSMlGEvUxncGybbbDaywuXGs35/RpNTD9HREREZWfb3zn47ajjxsiF1auhwNi+QZJjCzZl4dINs4sW+Xq3VKNPK3/7a4NRxKTV6cUa39iHg1AvLP/r9tEEI77ZrSuihY1aKWDq09pinaO0/vzzT6SlpQEAhg0b5nKfgTp16qBPnz7YuHGj0/Lff/8dgG1D6bwUSc4MHjwYY8aMQUZGBn7//XeXAYvevXujWrVqTsvuu+8++/NBgwZBqXSeMqVly5b25//++68kiLJ+/XoAtsBGv379XI4XALp27YpZs2bh6tWruHLliv2GelmbMmWKfbPxym737t146623ANhWl+Slw3ImJiYGw4YNg1Ao5UN0dDSefPJJbNiwAYMGDYLJZMJrr72GAQMGoFatWuU6fiJv+jdejZxcEaZcweMZFfJixDLn2/4QeQUDFkRElUSuTgaLxQq5HFAUupiQCwK0GtsFf4Cf48qpIH8ZtBorFHLHMrUyv62y0G8FQYC9TKN2bBuoltnLC5eqFPn9qpRczUVERERlx2DMnxhRlGqBVodjeRM5inOOgkSgWO0AwGyV1jOZi9dWrSz/SR4F91MouP+CM+3atXMZsDhx4gQAoH79+pL0R4WpVCq0bt0au3btsrdxpkmTJi7L8tIslaReVlaWpOzw4cMAAL1eD4Wi+LdCUlJSyi1gUVWcPHkSMTExMJvN8PPzw/fff4+aNWu6rF9w5YszjzzyCN59912888470Ov1WLJkCSZOnFjWwyaqMBLi/e3pn29n6/OY8SuAdB1QxMc4kccxYEFEVMnk6mSSdFAAUCtUjtnDXOe6fLV/kMuyrlFqdI1SOy0LVMuK7Hdk70CXZfc3VOH+hiqX5URERESlpVblT4woSpC/4+qBvIkcxTlHQQJQrHMCgEImradUFK+t2gOTPPJWVwAoMtAAoMibzrdu3XJbJ0/e7Pi8Ns4EBAS4LCu4CqS49SwW6ZLk69evux2nM3q9vlTtfMW///6LPn36IC0tDXK5HKtXr0a3bt3uuN+RI0di0qRJEEURu3fvZsCCqJxMHaKFCMcJiETexIAFEVEl4aexQh0sQpdmRZ063h5N6dSqBdye3EZERERUan1a+UvSNZVE4RRRxaVWCUVO5ChKy/oqyT5h3iSK+as4CqfkKaquK+76KG4/5S0vgNGgQQP88ssvxW7XoEGD8hpSpXf16lX06tULV69ehSAIWLp0KWJiYsqk77CwMFSvXh03btyw75VCRGWvcHCeqCJgwMLHRQ/JAFBJ73wS+bDKds0+5IM0aEJFGDIFAKX7ok9EREREd67gPhHXrl0rMsVSUasS8vpJSUlxe85r1645nNvT7rrrLvtYmjZtWqK0UOQoNTUVvXv3xsWLFwEAn376KZ577rkyPUdFCHQREZHn8Te0j9OEul8KTUQVQ94mWHI5PL4RFxERERFVDS1atLA/P3ToELp06eKy7qFDh1yW3XvvvYiLi0NCQgKuX7/uMr2UyWTCX3/9ZW/jLa1bt8amTZug1+uxf//+Mklb5KsyMjLw0EMP4dSpUwCAmTNnYsyYMWV6juvXr+PmzZsAgPDw8DLtm6ii6fRCBpT+VphyZACK3uOFyBcwYEFEVEmEhdk2w6pV2/Mbcd2pAa8qkJ1mBcyOeaSJiIiIyHPatGmD0NBQpKWlYeXKlXjttdecpnVKSkrCtm3bXPbTq1cvfPXVVxBFEUuXLsVbb73ltN7atWuRkZFhb+Mtjz76KKZNmwYAmDVrFgMWpaTX69GvXz8cOXIEADBx4kS8+eabZX6eRYsW2VdY8O+KqrrAuyyQyQG1xoLxK9IwdYhWkqpp2985+O2owW0/9WooHNIeLtiUhUs3zC7bpOtEdLhHhTp3yUudapGorDFg4eOMOcxVR1TZZOhFjF9h2yzx8Q4BeKCJn70sJc2Cj37JLFY/Ex8PgVaTH0DYc9KAXw/nuG1XUyvHuEeDJce++i0bZ6+aXLYJqQVYLIDZ/TUWEREREZUjPz8/DB8+HHPnzsXff/+N2bNn44033pDUMZvNGDlyJIxGo8t+YmJiEB4ejqtXr2L69Ol4+OGH0bJlS0mdK1euYNy4cQBsm2UPHz687N9QMUVHR6NPnz7Ytm0bNm3ahHfffRfvvfeey/oJCQmIi4vDkCFDym1MU6ZMsY9h2bJliI2NLbdzFUf9+vVx6dIlAM7TMRmNRsTExGD//v0AgFdeeQUffPBBic6RkJCAtLQ0tG7d2mWdDRs2YOrUqQAAtVrt1X83RB5x+7+bILMFEAr/7zMYRaTr3KdIqxbomEUlK8fqtm3cP0ZoNQIDFlRhMGDh45gSkqjyEUXYLziMZul/YotYvAsZALAWqmYwFa+tv8qxTrah6Isg9e34hiGTaeiIiIiIvG3y5Mn4/vvvkZiYiDfffBN///03nnvuOYSFheHs2bOYO3cuDh06hOjoaJdpoZRKJRYtWoT+/fsjKysLnTt3xvjx4/Hggw9CoVDgjz/+wMyZM+37YMyZMwfVq1f35Nt0sGzZMrRt2xbJycl4//33sXXrVjz//PNo0aIF1Go1bt68iWPHjmHLli3YsWMHBg4cWK4Bi7KSnZ2NtWvXSo6dP3/e/nzt2rWSP/tWrVqhVatWJT7PkCFD7KtuevbsiREjRuDEiRMu66tUKoc9UhISEtCjRw906NAB/fv3R6tWrRAWFgZRFHHx4kWsXbsWa9eutQdM5syZgwjmwyUvOHnZhOU7swEAD9/vj54t1JLySavSYTCJTif0rTugxx//5AIAXnkkCHXuyr/9eum6GQs2ZwEAujRXY0C0P4x6AepgEaIVCA0SUHhqsVolQKtxP+E4yN8xo0GQvwxajfvv4SEBzIZAFQcDFkRElYTtAkJ6oaFSSC9a5ELxLmQAQFaomlpZvLbBAY51AtWyItum60Qk/K2EKZO/doiIiIi8LSQkBFu2bEGvXr2QkpKC1atXY/Xq1ZI6w4cPR9euXYuc3d6vXz8sW7YMo0ePRnZ2Nt599128++67kjpyuRxTp07FSy+9VC7vpSTCw8MRFxeHwYMH49ChQzh48CAOHjzosn5wcLDLsookNTW1yL+n8ePHS16/++67pQpY/PTTT/bnO3bswH333Vdk/Xr16iEhIcFpWVxcHOLi4ly2DQgIwMcff4xRo0aVeJxEZcFkyZ/QZzA6Ts5L11lhMDmf0KcvsCLCUihWYLbml+UYpYW52QJmjw116K9PK/9Sr34onCKKqDLgnSMiokrincHuN9+qFSrH7GGOFzjF0TVKja5RavcVnRjZO7DI8jp1gKQkbhZOREREVFFERUXh5MmT+PDDD7Fu3TpcvnwZQUFBaNGiBUaOHIkhQ4Zg+fLlbvsZNmwYunXrhnnz5mHbtm24fPkyrFYrwsPD0bNnT7z88suSjb69rV69ejh48CDWr1+PNWvW4ODBg7h27RpMJhO0Wi0aN26MDh06YMCAAUVuSE6l06ZNG3zzzTeIi4vD4cOHkZycjNTUVJjNZoSGhiIqKgoPPvggXnjhBZcbuROVh3SdFVbRNrFPq5FBKc+f0FdwP4k8Wo0MBpPodEJfQIEVEfJCCxcUsvwyfxVXNRA5I4jOEhNSlZaYmIjIyEgAwKAJx/HjtHu9PCIiqup6P6+D2SpCIRPw21KNt4dDRFQhFbxGu3LlCurUqePlERGVzJ38Gz537hzMZjMUCgUaN25cXkMkIh/HzxpyZfyKNKTrRGg1QqknAZbW0JlpUAeLMGQKWPmWZ89NvqGyfc/gCgsfp1IzXkVE5a9WE6P9AgxgwIKIiIiIiIiIiBwxYEFEREREREREREQ+6/yfKphEKy4bZKgz0bPn7ur9LX6IKhQGLHxc+lX+EyCi8ifIpI9EREREREREFcWhnzRISvL2KIgIYMDC5/39cxDwubdHQURVnWiVPhIRERERERFVNDIZULu2Z8/597ogyOQiQrUC8JZnz01UETFgQURERERERERERD6vdm0gMdHTZ+XtWaKCmJyDiIiIiIiIiIiIiIi8jiE8IiIqdzK59JGIiIiIiIioomgVk4VWchGwCACCvD0cIp/GgIWP6/ZSGoA63h4GEVVxSn9R8khERERERERUUYRGmKEOFmHIFDx+7qMJRpjMgFIBtKyv8vj5iSoaBix8nOD5z2EiIiIiIiIiIiIC8M1uHdJ1IrQagQELIjBg4fNETnYmIg8wGwGl2vZIREREREREVJHk6mSwWK2Qy4HxK9IAACEBMrwzOERSb+UuHY5dcv/Ftl1jPwzuGCA5NmlVOgwmxxtxGXrenCMqiAELH2fM4RILIip/FqMApVqExcjPHCIiIiIiIqpY/DRWqINtgYN0Xd5Rq0M9Xa4V6Tr3AQZ9rmOddJ0VBtOdjJLINzBgQURERERERF4ll8thNpthsVggiiIE5q4lojImiiIsFgsAQCaTeXk05MwHP2Qg1yRi6tNayfEdxw3YfCQHABDbIxBRdZX2snSdFdPWZgAA7qunwtDuGknbL7Zm4UKKGQAw/VktlPL83y8Hz+ZibZweACC73aVoBUKDbHVCAhz/nWj8ZNBq3P+OCvBzrKPVyJyusMjj7HxEvogBCyIiKndHfwnErXQR1bQC8Ja3R0NEREQVjUqlQm5uLkRRhF6vh0ajcd+IiKgE8j5jANtnDlU8GXorDEYRqZkWVA+W248bjKJ9VYPJIr3hbxVhL9PlOq6IyMoRXa6IMJrzywTYAgy52QJmjw11OUZbQKR0v6MKB2KIyDmG7oiIqNzduqxE0mkVbl1Wuq9MREREPic4ONj+/NatW/abikREZSUzM9P+nEHRistgAj7bnC05plYJ0GpsPwVXSACATIC9TOPneJszyD+/bWEqRX4Zf+sQVRxcYeHj5Ep+JBNR+ev0QgaU/laYcmQAQtzWJyIiIt8SGBgIQRAgiiKys7ORmJiIatWqISAggOmhiOiOWCwWpKen4+bNm/ZjgYGBXhwRuVJTK4e/SoRGLf3c79lCjZ4t1E7baDUyzB7mekXEiw8FuSx7oIkfHmjiBwAYOjMN8C/FoImozDFg4eMUnOxMRB6gUluhDhIhiI5LdImIiIhkMhkiIiKQlJRkD1pkZ2dDEATI5XL3HRAROVFw34o8NWrUYEqoCmrco8HuKxFRlceABRERlTujQYDFCliMnCFJREREzgUFBUmCFoDtZqPZbPbyyIioqggJCcFdd93l7WGQC23bAikp3jm3f3gA5EoRQRruu0jkbQxY+Lhz+50vqSMiKksqtQh1sAhDpvu6RERE5LuCgoLQpEkTZGdnIzMzE0aj0WF2NBFRScjlcgQEBECr1UKt5j2QiiwlBUhK8tLJk2ypoSIivHR+IrJjwMLHJR1lgj4iIiIiIqo4ZDIZgoODJRtxExGR75DJgNq1vXPuWrW8c14iyseABREREREREREREXlV42561DOLUCkE7Pw2wNvDISIvYcCCiIiIiIiIiIiIvKpOi9zbqYQFAJ4NWKSkWWARRcgFAbVC5R49NxFJMWDh48Ia5Xp7CETkA+R+ouSRiIiIiIiIqKL46JdMpOtEaDUCZg8L9fZwiHwaAxY+rnkfvbeHQEQ+QKGUPhIREREREREVdGhNEFJvAtXvAvCWt0dDRN4i8/YAiIiIiIiIiuv69evYsGEDJk+ejIcffhjVq1eHIAgQBAGxsbHF6mP58uX2Nu5+li9f7rY/vV6P2bNno127dqhWrRoCAwPRrFkzjBs3DpcvXy72ezt58iRefPFFNGrUCP7+/qhRowa6du2KL7/8Emazudj9fPfdd3jooYdQu3ZtqNVq1K9fH0OHDsWBAweK3QcREZGnZd9Q4OYVBbJvcH41kS/jJ4CPs1i8PQIi8gXGHAF+GhHGHMHbQyEiokquZs2a3h6CxIULF9CvXz/8888/kuNnzpzBmTNnsHjxYqxatQp9+/Ytsp8lS5ZgzJgxyM3NT9lqMBiwd+9e7N27F8uXL8eGDRtw1113uezDYDBg8ODB2LBhg+T4pUuXcOnSJaxatQpTpkzBpEmTSvFOiYiIylfH4RlQqK2Qy4HxK6RlTcKVGNk7UHJszvpMXEt3f2Orf1t/dI1S21+n66yYtjZDUidDz/TFRBUFAxY+zpzLm4dEVP5Ei/SRiIioLERGRqJZs2bYtm1bqfvYunUrwsPDXZbXqVPHZVl2djYeeeQRe7Bi5MiReOqpp+Dv74+dO3dixowZyMjIwODBgxEXF4f77rvP5RhGjRoFq9WKmjVrYuLEiXjggQdw69YtfPXVV/jpp59w4MABDBo0CDt37oRM5nyh/IgRI+zBih49euCVV15BeHg4jh8/junTp+PChQuYPHkyateujRdeeKG4f0REREQe4aexQh1sCxyk66Rl2QarQ/1MvYh0nftAg8EkrWMVUax2ROQdDFgQEREREVGlMXnyZERHRyM6Oho1a9ZEQkICGjRoUOr+mjRpgvr165eq7Zw5c3DmzBkAwKxZszB+/Hh7WYcOHdCjRw907doVer0er776Knbs2OHQh9lsxtixY2G1WhEcHIz9+/ejYcOG9vL/+7//w5gxY7Bw4ULs2bMH33zzDZ577jmHfnbv3o1Vq1YBAPr3749169ZBLpcDAKKjozFgwAC0adMGly9fxhtvvIHHH38cWq22VO+biIiqrg9+yECG3hYcqFdDgbF9gyTlq/bq8NdFIwDgjZhg1AiW28v+STJh8e/ZAIDeLdXo08pf0nbKdxnQ5VpRPUiONwcFS8p+OZRjD1aIViA0SDrBNlDtGKwPDhCQY3Q/EVetlNaRCYBW47xdSACz5xN5GwMWRERU7nYuCEVSEhARAW6eRkREd+S9997z9hAAACaTCfPnzwcANGvWDK+//rpDnQ4dOmDEiBH48ssvsXPnTvz5559o06aNpM66detw/vx5AMDbb78tCVbkmT17NlavXo20tDTMnj3bacBi1qxZAAC5XI6FCxfagxV5qlevjg8//BBDhgxBWloalixZ4nTMRETk2zL0Vvvqg2qBjqsa9Ib8VQ1ioUUKJkt+mcHouIIhQ29FtkGEUu7Yb45Remz2sFC3Yx33aLDbOs5oNbJi9U9E3sGwYQU0a9Ys+yZ/3BiPiKqCwBpm3BVpRmCN4m8YSkREVJHt2rUL6enpAIBhw4a5TNNUcCPwn376yaH8559/dlq3oICAADzxxBMAgBMnTuDcuXOS8uzsbGzfvh0A0Lt3b5dprAYNGoTg4GCXYyEiIpo9LBQzng1BnbvkCPJ3/N0WoBag1dh+hEKLFJTy/DK1ynEFQ0iADFqN4HQVg79KBvF2zKJwIISIfAtXWFQwp0+fxuTJk6HRaKDT6dw3uENKNX8LEFH5i34yC+pgEYZMAQBnshARUeW3d+9e+/Nu3bq5rNe2bVv7tf2+fftc9nPPPfegVq1aLvvp1q0bvvzySwDAvn370LhxY3tZfHy8fbPuosaiUqnQvn17bNu2DfHx8TCZTFAqlS7rExGRb6oeLMe7T4Y4LXu6iwZPd9E4LbsnQlnkyoUpTznvEwAGRPtj7keAwWSFWikDXi7ZmImo6uAKiwrEYrFg2LBhaNmyJWJiYjxyThcTwYiIiIiIfEJsbCxq1qwJlUqF6tWro3379njnnXeQlJRUZLvTp0/bnzdt2tRlPYVCYU/zVLANYFsZkZiY6LaPwuWF+ynuWAqWm81mh5UaRERE3nR+nz8O/qTB+X3+7isTUZXFFRYVyIcffoijR4/iyJEjmD17treHQ0RUZnTpcugyRcCavyx4zvpMXEu3QK0UMPVpraT+juMGbD6SAwCI7RGIqLr5sz/TdVZMW5sBALivngpDu0tn93yxNQsXUmypp6Y/q4VSnn/Og2dzsTZODwB4vEMAHmjiZy8zWURM+CYdANCwlgIvPiTdXG7lLh0u3TDjncGuZwUREVHls3v3bvvzmzdv4ubNmzh48CA++ugjzJs3D6NHj3ba7sqVKwAAjUbjdvPqyMhIHDt2DDdu3EBubi78/Gy/fxITEyHeznvhKo1TwT4Kn9vZ65L207x58yLrF5QXXHElOTm52H0REVHF1LYtkJLinXPz1wgRAT4esLh+/Tri4+MRHx+PQ4cO4dChQ7h58yYAWx7a5cuXF7uvy5cv45NPPsHGjRtx+fJl+Pn5oVGjRnjiiSfwn//8BwEBAUW2P3HiBN577z288847iIqKupO3VSK7FoYCkzx2OiLyURqtpUBKKJtMvW1DNrXSMTWdwZi/WZvJIi23irCX6XIdN2vLyslvW5jRnF9mNDvWySvLypGWpWZaoMu1IkPveD4iIqqc7r77bgwaNAgdOnSw38S/ePEifvzxR6xduxYGgwEvvvgiBEHAqFGjHNpnZWUBAAIDA92eS6PJD65nZ2fbAxZ5fRSnn8J9OBvLnfbjTsFgBxERVU3yGrkI1ogwGwWcO+DnvgERURnz6YBFzZo1y6SfjRs34plnnkFGRob9mF6vtwdBFi9ejE2bNuHuu+922t5sNiM2NhbNmjXDW2+9VSZjIiKq6IIDBOQYBaiVjpuxqVW2jdoASFZIAIBMgL1M4+eY1y7IP79tYSpFfplK4VgnryzIX1r22eZsWBirICKqMmJiYjBs2DAIhXYLjY6OxpNPPokNGzZg0KBBMJlMeO211zBgwACH/SUMBgMA274Q7uQFKAAgJyfHoY/i9OOqj7Lsh4iIqEk3PdTBInRpAvRXvBOwKGJLJyLyAT4dsCgoMjISzZo1w7Zt20rU7ujRo3jiiSeg1+sRGBiIt99+Gz169EBOTg6+++47fPXVV/jnn3/Qr18/HDp0yOmMp+nTp+Po0aM4ePAgN70joirNYgHyM1UE248ve6NwTfXtH2C1Qy8y5G3cvRrA2w7l+amcVk8sXOZ3+8dZv9INwT8oUNJjrBXqYBFGnfNACBERVS4hIUWn93vkkUfw7rvv4p133oFer8eSJUswcaL0l4pabfs9ZTQa3Z4vb0NsAPD3z8/LnddHcfpx1UdZ9uNO4VRUhSUnJ6Ndu3Yl6pOIiComuRxwkwmwzI1fkYZ0nXh7IpnrzbuJqGrz6YDF5MmTER0djejoaNSsWRMJCQlo0KBBifp49dVXodfroVAosG3bNnTo0MFe1rNnTzRu3BhvvPEGzpw5g7lz52Ly5MmS9kePHsUHH3yAcePG4f777y+T90VEVJG52cO0QvpxRggEGVArDMB4b4+GiIg8YeTIkZg0aRJEUcTu3bsdAhZBQbYAeXHSKul0OvvzghOY8vooTj+u+ijLftxxtz8GERFVfqd+0yAzS0RwkAAwCQgReYFPByzee++9O2p/6NAh7Nq1CwAwYsQISbAiz+uvv45ly5bh9OnTmDdvHt5++23JKophw4ahYcOGmDJlyh2NpbSiHi5Z3loiotKQyW2PcjkQEeHdsZRGcrIMViuQq3Ffl4iIqoawsDBUr14dN27cQJKTaHudOnVw8OBB6HQ6pKenF7nxdt7KhBo1akhSMhUMALjb0Lrg6obCe0kU7qdt27al6oeIiOjaPyokJVXO721EVDX4dMDiTv3888/258OHD3daRyaT4bnnnsPbb7+NtLQ07Nq1C71797aXHz16FIB0GXdBeUGQdevWYeDAgWUz8AJqNDCVeZ9ERIWFhQHpOqBWbc8vKy4LdepUzpUhRER0Z0RRdFnWvHlz/PjjjwCAM2fOoH379k7rmc1mXLhwAQDQrFkzSVlgYCAiIyNx5coVnDlzpsixFCwv3E/z5s2d1iuqH4VCgUaNGhVZl4iIiIjI0xiwuAN79+4FAGg0GrRp08ZlvW7dutmf79u3TxKwGDFihNM2e/bswblz5zBgwADUqFED9evXL5tBExF5UYZexFe/ZWNkb2kKijnrM3Et3eK2ff+2/ugalR/gTddZMW1tRrHO/fqAYNQKldtfHzybi7VxerftQgJkAIrOdU5ERFXP9evXcfPmTQBAeHi4Q3nnzp3tz3fv3u0yYHH48GF7GqZOnTo57Wf16tX4559/kJKS4rC5d8Fz5CncT3R0NFQqFYxGI3bv3o233nKew8NoNOLAgQOSNkRERAV1HJ4BhdoKuRwYv8KxvEtzNQZES/dAGr8irVh9v9ArEPdE5Gcd+SfJhMW/52f+yNC7nihARL6DAYs7cPr0aQBAo0aNoFC4/qNs2rSpQ5s8ixcvdtomNjYW586dw9tvv+3yy09ZMOVyA1ki8hxRBLINVofjmXoR6Tr3F6cGk7SOVUSx2gGApdAsWaO5eOcErKjewAR5NRGhIQIApdsWRERU+S1atMi+wqLgBKQ83bt3R0hICDIyMrBixQq88cYbEATHa+vly5fbn8fExDiUDxw4EKtXr7bXdRZs0Ov1+P777wHYVlM0adJEUh4UFIQHH3wQmzdvxu+//47ExESn+0389NNPyMzMdDkWIiIiv0Ar1EG233/pOsfyHKPj97nificzWUSH18VtS0S+gwGLUjIYDEhNTQXgfvO50NBQaDQa6HQ6Sc7Y8uIu/21ycrL9udX9hGYiojtmW6Vgu7ANVMscyoMDBOQY3QdQ1UppHZkAaDXFC7zKC91EUimEYrUNCZChRb9sqINFGDIFAKHFOh8REVVMCQkJSEtLQ+vWrV3W2bBhA6ZOnQrAlrrVWfpXlUqF//73v5g6dSpOnz6NOXPmYPz48ZI6cXFxWLJkCQBb0CM6Otqhn5iYGDRs2BAXLlzAjBkzMHjwYDRs2FBSZ/z48UhLS7M/d2bcuHHYvHkzzGYzxowZg59++glyef7KwtTUVLz55psAAK1WixdeeMHl+yciIt/lp7EFEEQRCA10/L7kr3L8Plfc72RKueDw2llb2/dHIvJVDFiUUlZWlv15YGBgETVt8gIW2dnlv8k1N88joormncFFp1Qa92hwqfrVamSYPax0AYQHmvjhgSZ+7isCGDqzeEuciYio/O3btw/nz5+3v86bRAQA58+fl6xoAGwrlwtKSEhAjx490KFDB/Tv3x+tWrVCWFgYRFHExYsXsXbtWqxdu9a+umLOnDmIcLHz6Pjx47FmzRqcPXsWb7zxBs6fP4+nnnoK/v7+2LlzJ6ZPnw6z2Qx/f3/MmzfPaR9KpRKffPIJ+vfvj8zMTHTq1AnvvPMO2rVrh7S0NHz11Vf2vTI6d+6MoUOHOu2nZ8+eeOqpp/Ddd9/hl19+Qe/evfHqq68iPDwcx48fx7Rp03D58mUAwMyZMxEaygA8EVFFl5JmwUe/2FbGtWvsh8EdAyTl837NQtItMxRyATOe1UrK9pw04NfDOQCAZ7tp0LJ+fhrAbIMV762xpdZtHqnE8J7597VEERAAQESxv2uV9jvZPRHKUrcloqqLAYtSMhgM9ufFyf3q52e7KZaTk1Os/pcvX+7wZYuIiLxDnyGHPssKWDnTh4jI2xYvXowVK5wk1Qawf/9+7N+/X3KscMAiT1xcHOLi4lyeJyAgAB9//DFGjRrlsk5QUBA2btyIvn374ty5c1i0aBEWLVokqRMcHIxvv/0WrVq1ctlP37598cUXX2Ds2LG4du0aXn75ZYc67dq1w7p16ySrJgpbunQpMjMzsWnTJuzcuRM7d+6UlMtkMkyaNAmjR4922QcREVUcFjE/ZZI+1zF1UlaOFek6EQqZY5nBlN/WZJaWiQVS6+oM0rY3LigREm6G0o+pmojIOxiwKCW1On/TV6PR6LZ+bm4uAMDf399NzTvnLu1UcnIy2rVrZ3vBLSyIiNwKCLHcTgnFi3YiosquTZs2+OabbxAXF4fDhw8jOTkZqampMJvNCA0NRVRUFB588EG88MILCAsLc9tfo0aN8Ndff+Gzzz7DDz/8gPPnz8NoNCIyMhJ9+/bFK6+8gnr16rntZ+TIkejQoQM++eQTbN++HVevXoVGo0GzZs3wzDPP4IUXXihy3zzA9l1j48aNWLVqFZYvX46jR48iPT0dNWvWRJcuXTB27Fh06NCh2H9WRETkXXIhP2VSgJ/jDZwgfxm0GisUcscytTK/rbLQrw+hQGpdjVra9sjaIDTunY0adZhDnIi8QxBFkXdfbktISECDBg0AAMOGDStyhYPBYLAHH/r164cNGzYU2XdgYCB0Oh3at29f5EwuT0hMTLSnjXrq/WNYPamFV8dDRFTRDZ2ZZt/DYuVbXLJMROWj4DXalStX3O6TRlTR8N8wEVHlV6cOkJQEREQAbrZIJaJKorJdozG3RSmp1WpUr14dgPtNrtPS0qDT6QBwfwkiIiIiIiIiInLtQooJ/ySZcCHF5O2hEBF5HFNC3YFmzZph7969OH/+PMxms8sl2mfOnJG0qUgM2YxZERERERERERFVFO+vzIZKY1vhvXOBZ1d4Jyd79HRERA4YsLgDnTt3xt69e6HT6fDnn3/igQcecFpv9+7d9uedOnXy1PCK5cDXIcCH3h4FEVHFptKIkkciIiIiIqLyYr29fYTFYkvP5El9/5sJ/2AR1lwBQLBnT05EBAYs7sjAgQMxY8YMAMCyZcucBiysViu+/vprAIBWq0WPHj08OkYiIrpzgiB9JCIiIiIiKi+X/lQjxyjCnCsgIsKz565W2wJ1sAijjl9+iMg7GLC4A+3atUOXLl2wd+9eLFmyBMOGDUOHDh0kdT766COcPn0aAPDKK69AqVR6fJxRUVGS1yYTcyASEZWE1QrIZbZHIiIiIiKi8nQxzt9rG1+PXwGk64CwMM+el4goj08HLPbt24fz58/bX6emptqfnz9/HsuXL5fUj42Ndehj/vz56NSpE3JyctCnTx9MmDABPXr0QE5ODr777jssWrQIANCkSRO8/vrr5fI+iIiofJn0AuTBIkx6zjIiIiIiIiIiIiovFS5gkZubi3379iE1NRUNGjRAu3btyu1cixcvxooVK5yW7d+/H/v375cccxawaN26NdasWYNnn30WmZmZmDBhgkOdJk2aYOPGjQgKCiqTcZfUyZMnJa8TExMRGRkJAOgwPB1AHc8PioiIiIiqFE9exxMRERERUdXk0YDFpUuX8NlnnwEAJkyYAK1WKyk/cOAAHn/8cSQnJ9uP3X///fjxxx9Rt25dTw61RPr3749jx45h/vz52LhxIxITE6FSqdCoUSMMHjwYY8eORUBAgLeH6ZSfPzeQJSJy5+opP5gsIpRyrrAgIt9UVa/jiYiIiIioYhFEUfTYHet58+bhf//7H+6//34cPnxYUpaVlYXGjRvjxo0bKDykqKgo/PXXX1AoKtyCkEqp4AqLp94/htWTWnh5REREFVudOvBaDlki8h0Fr9GuXLmCOnUqzipYXsdTcVTkf8NERJXJ8Hm3IFMAEIFqwa4nTT3eIQAPNPGzv05Js+CjXzKLdY6Jj4dAq5HZX+85acCvh3OQoRchioBWI2D2sNBSvwciqjgq2zWazH2VsvPbb79BEAQMHDjQoWzRokW4fv06AOC///0v1q9fj//85z8AgFOnTrlM3UR3JjeHs4WJiNxp3E2Pjk/o0Lib3ttDISLyCl7HExEReY5cAchkgEwOpOtElz9Gs3SigEV0Xbfwj7XQ9GWDyXbcc9OaiYic8+hUp4sXLwIA2rRp41D2/fffQxAExMTEYN68eQBsqZZu3LiBH374AWvXrsWIESM8OVzfwF9ERERu1WmRC3WwCEOmAKBipvgjIipPvI4nIiLyHIsZUKhsz7Ua1xNNVQppmVwQiqxfkKxQNbVS2jYkwKNznImI7DwasMibeVWzZk3J8czMTBw5cgQAMHz4cEnZU089hR9++AFHjx71zCCJiIiIiEiC1/FERESe89ucaqVKSVsrVF7qNE5do9ToGqUuVVsiorLk0XBpVlYWAMBisUiO79+/HxaLBXK5HN27d5eU5eXXunXrlkfGSEREVJjVKsCUa3skIvJFvI4nIiIiIiJP8OgKi5CQENy6dQtXr16VHN+1axcAoGXLltBoNE7bqtWM8pYHmdzbIyAiqvhkMhFKP8CSyzx6ROSbeB1PRES+5oMfMpCht+LdJ0MQqM6f73s0wYhvdusAAP3b+jusSnj7m3SYLSIiqinwav8gSdkPf+gRfy4XAPD6gGDUCs2/KXMhxYQvtmYDAO7uoEbSWv9yeV9ERBWdR1dY3HvvvQCAdevW2Y9ZLBZ73tsePXo4tElKSgLguPycii8qKkry07NnT3uZ0o8334iIiIioaLyOJyIiX5OhtyJdJ+JGhlVy3GTO3wjbYHK8p5KebWuXlWN1KNPn5m96bSm0u7XZkt+vgvdqiMiHeXSFRUxMDHbv3o2VK1eiZs2a6NKlC1auXIlLly5BEAQ88cQTDm0OHz4MAKhbt64nh0pERERERLfxOp6IiHzVwi1Zkn0hlIr8jbDVSseUsdpAGcwWEUH+jnOEA/zyN7aWC9K2Cnl+v3fVM6HniCwoBBkA5ysYiYiqKo8GLEaPHo0vv/wSp0+fxpw5czBnzhx7Wf/+/dG2bVuHNuvWrYMgCA45can4Tp48KXmdmJhozymcclbljSEREVUqglz6SETka3gdT0REvqZ5pBI6gwiNWhpYaFlfhZb1Xd9LmfGs1mXZ4I4BGNwxwGlZw1pKe2Bk6Mw03B1hgSFTAAMWRORrPBqw8PPzw/bt2zF27Fj8+uuvMJlMUKlUePLJJ7FgwQKH+nv27MGpU6cgCAJ69+7tyaH6jDO/8xcfEZE7Kn9R8khE5Gt4HU9ERL7mszcCkZJiez7Jw+fu+pKHT0hEVIF4NGABALVq1cLatWuRm5uLW7du4a677oJK5TwyHRkZiZ07dwKA01lbRERERETkGbyOJyIiX5KSAtzejomIiDzI4wGLPH5+fqhdu3aRdRo0aIAGDRp4aERERETOmU2A0s/2SETk63gdT0REvkQmA9z82itz8tupaGVMSUtEPsijAYvnn38egiDggw8+cPslJ8+NGzfw5ptvQhAELFmypJxH6HtU/lZvD4GIqMKz5ApQ+omw5DpuqkdE5At4HU9ERL6qdm0gMdGz5xy/AkjXAWFhnj0vEVFF4NGAxfLlyyEIAl5//fVif9HJzMy0t+MXnbLXYXiGt4dARERERBUcr+OJiMjXtByQjRYyK2RWGYBAbw+HiMhneC0lFFUMnCtMROTeiS0aZGQCIcEA3vL2aIiIiIiIqLxVq2uCOliEIZN3ToiIPKnCBywMBgMAW65cKntW0dsjICKq+G6cVyEpCYiI8PZIiIgqD17HExFRVeAXKGL8ijS39cY+HIR6Yfm32Y4mGPHNbp3bdmqlgKlPayXH0nW8WUNEvqvCByz2798PAKhZs6aXR1I1mXI4U4CIyJ0HhmZCrrbCYpABCPb2cIiIKgVexxMRUWW2Z5EWqbdEPD09vVgBBHOhGaEmc/ECD2qlY51mdRQ4nWgu/mCJiKqQcg1YvP/++06PL1y4EGFudg7Kzc3FhQsX8Msvv0AQBHTq1Kk8hugToqKiJK9NJpOXRkJEVDkFhFhuLwfnTCci8g28jiciIl9nMQowGQBDpoBaxdi+SSGTTghVKgCtxv0kUbXSsU5kdQWS0ywICZAVe7xERFVFuQYspkyZAkGQfvCKoojPP/+82H2Iogi1Wo3x48eX9fCIiIiKxWwWkKsTYTZzVRoR+QZexxMREQGmXAE7F4QiMbHkbVvWV6FlfVWpzju4YwAGdwwoVVsiosqu3FNCiWL+bNS8Lz0Fj7miVqtRu3ZtdOzYEePGjUPLli3LbYxV3cmTJyWvExMTERkZ6aXREBFVPgqFCD8NIFq4woKIfAev44mIiIiIyNPKNWBhtVolr2UyGQRBwIkTJ9C8efPyPDUVk0LFm29EREREJMXreCIiqig++CEDGXorNH4yTHkqRFK27e8c/HbUAAB4oVcg7olQ2stuZFowa10mAKD13So83UUjabtgUxYu3bDtEzF7WKikbP+ZXDw8QYer/yhgzZUBCCzrt0VERC54dNPtunXrQhAEqFSlWxJHZU9e4bddJyIiIiJv43U8ERF5S4beinSdiHSdxaHMYBTtG1ubCq2GFsX8Ta/1BsfJmlk5VpebYhtNtuPh95ghWp1WISKicuLR29UJCQmePB0REVGZUmlEjF+RVuazt34+qAcADOmswf0N828GGowiJq1OBwA0CVdiZG/pzK5lO7Jx6ooJAPDukyEIVOdvync0wYhvdusAAP3b+qNrlLrU75uIiNfxRETkLdWD5MjUm2F1EltQqwT7xtZKuXTvJUHI3/Q6QO24F12QvwxajfNohEopQLQCgswW+CAiIs/h/Hofd/RXDTDJ26MgIqrY5LfT54lW2yytsp69lVdmNBfqF/n9Zhscv0zpDPltC3+RMpnz2xpM/JZFRERERJXTm4OCXZb1aeWPPq38nZbVCJY7TBYqaGzfIJdlnZr64Yuf9VAHizDqHIMdRERUfhiw8HFpV7isn4jIHfntxRQyuW2WVlnP3sprq1IU6hf5/RZcPZFHo85vKxQ6rVKR31at5JcsIiIiIqqc2rYFUlI8f96uL3n+nERE5KWAxenTp7Fo0SLs3bsXFy9eRFZWlsPGfoUJggCz2eyhERIREeUz6gSog0XkZguYPdZxltadzt7q1NTPaZlaJRTZ7/Cerjf/a1lfhZb1GZQmorLF63giIvK0lBQgKcnboyAiIk/xeMBi7ty5ePvtt2E2myEyESAREVUiFgtQp463R1E6tWoBhw97exREVJnxOp6IiLxJJgNq1/bc+eTy2+eVe+6cRETk4YDFli1bMG7cOAC2mVbt27dHmzZtUK1aNchkjqkuqPzVj87x9hCIiCoVzu4iIl/E63giIvKWRp1zUMdkhVopw67vnO9XUR7GrwDSdUBYmMdOSURE8HDAYt68eQCA0NBQ/PLLL+jUqZMnT09O1I82eHsIREQVXt6sKrkciIjw7lhKqvOodASEWJGbLQOg9fZwiKiS4nU8ERF5S2QrA9TBIgyZAgDPBSyIiMg7PBqwOHz4MARBwOTJk/klx4OioqIkr00mk5dGQkRUOX35ihYibJtgq98Ctv2dg9+Oug/41quhcNi/YsGmLFy64T6Xe++WavRplf+FzGAUMWl1erHGO/bhINQLs/2KHzpThFwJyORM30JEpcfreCIiIiIi8gSPBiz0ej0AoHPnzp48LRXBzNgFEZFbapUgeW0wikjXuQ8AVAt03Ig2K8darLYGo7SOCBSrHQCYrfn1cnUyGHNFiGahiBZEREXjdTwREXnLn2uDcPOWiLqNrRhfO61YbaYO0Uqu4Usz4aheDQWqBVoR5M/Uh0REnuTRgEVERAQuXrwIo9HoydP6vJMnT0peJyYmIjIyEgBgMfEGFhFRSalVArQa95+fzr7cBPnLoNU4BjKcnaMgASjWOQFAIcuv56exFlhCT0RUOryOJyIib8lMUeB6ElCjhqnYE3gK1yrNhKPCK6WJiMgzPBqw6N+/P+bPn4/9+/ejQ4cOnjw1ERFRmenTyl+SrqkkSvvFR60SMHtYaKnaEhHdKV7HExGRt1ktxZs0BNgm+xR0JxOOiIjIszwasBg3bhy+/vprfPTRR3j22WdRq1YtT56eiIiIiIhKgdfxRETkbZkpilJP4LmTCUdERORZHg0dh4eHY/369bBYLOjYsSM2bdrkydMTEREREVEp8DqeiIi8pW6bHLTolYO6bXK8PRQiIvIAj66w6NmzJwCgWrVqOHv2LPr37w+tVovGjRsjICCgyLaCIGD79u2eGKZPUfkXL/8jERFVTgo/UfJIRFQavI4nIiKTRcSEb9IBAA1rKfDiQ9JUpyt36XDskm2vo4mPh0CryZ8je/KyCct3ZgMAHr7fHz1bqCVtJ61Kh8EkoqZWjnGPBkvKoh6yBSpEKwBwlQQRUVXn0YDFrl27IAj5OQNFUURaWhri4+NdthEEAaIoStpR2eEfKxFR1SZXSh+JiEqD1/FERATAvnF1Vo7jZBhdrtVebi1UbLLkb3ptMDq2TddZYTAB/irHMosZkHv07hUREXmTRz/yu3btyi8sFYzF4u0REBEREVFFx+t4IiICYN+4Osjf8XeCxk9mL5cVKlbK8ze9Vqsc22o1MhhMIoIDHMtECwAFIHLBMBGRT/D4CguqWPZ+GQpM8fYoiIiovBj1AvwCRRj1vNFIRKXH63giIlLKhSI3vR7aXQNA47Qsqq6yyLZTn9a6LDPnClD4iTDqeD1LROQLuKiOiIioCrPl+s1/JCIiIiIqqbZtgZQU75y760veOS8REXkHAxZERERVmNkkwJgjwmzijDQiIiIiKh2hWi4C/USYjQIuHPbz9nCIiKgKY8CCiIioCtv7pRZJSUBEBIBJ3h4NEREREVVGTXvqoQ4WoUsTYEj2bMBCLrc9yuQePS0REXmJRwMWe/bsuaP2Xbt2LaORUJ77H8/09hCIiKgcaSNMsAYA2lAAUHp7OERUSfE6noiIAFvwIDHRs+ccvwJI1wFhYZ49LxEReYdHAxbdu3eHIJQuJYUgCDCbzWU8IgoOs3h7CEREVI5ax2RDHSzCkCkAcL3RIRFRUXgdT0Tk287sCECWTkSQRgDe8vZoiIioKvN4SihRFD19Sp8XFRUleW0ymbw0EiIiIiKqrHgdT0Tku5JP+eWnGfUwtVKAWilCreSebEREvsCjAYudO3e6raPT6fDPP/9g9erVOHz4MDp27IipU6dCJpN5YIS+x2jgL3wioqpMny6HLlOEYOXnPRGVHq/jiYiooINnc7E2Tu+2XkiADO8MDpEcW7lLh2OXjG7btmvsh8EdAzD1aW1ph0lERJWQRwMW3bp1K1a9vn374rXXXsPMmTMxYcIEfPXVV1i1alU5j67qOnnypOR1YmIiIiMjAQCi1RsjIiIiTwnQWgqkhCIiKh1exxMRUUFGs4h0XXFW3jnedNDlWovVVp/LlX1ERL7I4ymhSuKtt95CfHw81qxZg/79+2PIkCHeHhIREREREbnB63giorL3wQ8ZyNDbAgAd7/FDTPsASfmc9Zm4lm6BWik4rErYcdyAzUdyAACxPQIRVVdpL0vX/T979x0dVbX2cfw3k15JQm8C0sGGdEEQFQuICKJSVOAKlldU7kVQsd8rNryKXREFCwJXRZRiQ+mCFLFRpChKQhcSSM9k9vtHzEkmM5n0TJL5ftaalcnscp7JCWHPec7e26lpHyZJks5qFqwbLohwafvaF6fU5zaH1s6LUHiQXVKgggNtioko+oaYWuHus+wiQuzFahsewg03AOCPqnTCQpLGjBmjRYsWaebMmXzQAQAAAKoJxvEAUL6SUvNmJqRmus8+OJmaM+shNMi9LD0zb0ZEVrZrudPIKkvJcJ8RcSrNKCLW6NL/S/57lYY4dW8Tou5tQkr1PnISIhFF1gMA+Kcqn7A47bTTJEm//PKLjyOpmWzcsAAAAIAKwDgeAMrXXVdE6UiiU699mazwYPcP89HhNqVl2jxuTh0anDcjIijAtdxuk1UWEeI+IyIqzCbjlGx2ybBKEwCgglX5hMXhw4cl5Wzih/IXHMZoAwBqsuAI4/IVACoL43gAKF9NageqSW1p5m1xHsvvHhxdaNsLzwzVhWeGeiyLibBr+ujYQtveemmUbth6QqHRRpkp3PUIAKhYVT5h8fLLL0vKu0MLAAAUX+5MOmbUAahsjOMBoHx16SIdOuSbY/e5zTfHBQD4nyqZsDhx4oQ2b96s5557Tp9//rlsNpuGDh3q67BqpJNHA3wdAgCgAhkj2cT0fQCVg3E8AFScQ4ekhARfRwEAQMWq1IRFQEDpLo63bt1a99xzTzlHA0n6/oNo6QVfRwEAqCiZKTam7wMoM8bxAOB70Q0cygo2Mk6bgh2Ve/9p7n8Ddu55BABUsEr9H86U8PbOwMBADRs2TDNmzFCtWrUqKCoAAAAA3jCOBwDf6zzslEKjjdJP2vTuvYXvOVERJr8tJaZI9epV6mEBAH6oUhMWDz/8cJF17Ha7oqKi1KJFC/Xq1Ut16tSphMgAAKiZjuwNlsM4FWiz+zoUANUY43gAAAAAlaHKJSwAAED52fZZhBISpMaNfR0JgOqMcTwA+N7+H0KVnuVUo5bZmvz2iSLrt2kUpPH9I11ee+aTkzqcmF1k20FdwtSnY6j1fbaz5PECAFAaVXLTbVSe8285IamJr8MAAFSQ03umqWGmUViwTVKYr8MBAACocXKTB83qBmrCgCiXsvfXpGjrb5mSpClDolU3Om8TiF8TsjRrebIkqf/ZobrkHNex2iPzk5SS4VSdqADdMzRae9aGKSFBuujGFCU2dxQZV3K6e5bhZKpRYkrRy/ylZ7nWue2ySD398aki2wEAUFYkLPxcKfdPBABUE806p1trHZOwAAAAKH+5CYC4SPcEQWp6XoKg4HZAWdl5ZemZ7kmEpFSnktONggJc+3Vk2BUTYSsyrshQ9yVBo8NtSsssum1okGudutEBiomwqVY4y4wCACqWTxMWhw8f1sqVK/XLL7/o+PHjkqS4uDidccYZuuCCC1S/fn1fhucXSrZ9IgAAAMA4HoD/WrczQ5lZRsFBNvVqFyJJVvIgKsz9Yn54qM0qtxXIEwQF5JWFBrsnEWqF2xUY4HRLEuxZG6aV80t3I8rdg6NL1S4mwq7poyt3o28AgH/yScLi4MGD+te//qWFCxfK4fA8jTEgIEDDhg3Tf//7XzVs2LCSI/QfmalF31kBAKi+HJk2pZ2SsotxJx0AFIVxPAB/N3NxqoIjcmavXvdSyN+v5l3If9KtRcTfD2ne/QXLgqy28zwerZb17MU7pYMHSxk0AADVSKUnLH788UddfPHFOn78uEzB+ZD5OBwOLViwQMuXL9fXX3+tM888sxKjBACgZggMNgqNMkpnSh2AMmIcDwCS8+/9qrOzpYSEyj32iMdOKCI2d6lPZjsAAGqmSk1YpKSkaODAgfrrr78kSRdffLHGjx+v7t27q0GDBpKkQ4cOaePGjZo1a5a+/PJLHTt2TAMHDtTOnTsVHh5emeHWGB07dnT5Pisry0eRAAAAoDpiHA8AObZ/FaGTp4yyHTY1bly5x87dg9LOXpQAgBqsUhMWL730kg4cOCC73a7XX39dN910k1ud0047TaeddpqGDRumt956S+PHj1dCQoJefvllTZ48uTLDBQAAACDG8QCQ6/CvwUpIkBo3luLjK/fYk9+WElOkevUq97gAAFQm9x2hKtAnn3wim82mMWPGePyQU9A//vEPjR07VsYYffzxx5UQYc20bds2l8c333xjlQX4dNt1AEBFs9ldvwJAaTCOBwAAAFAZKvVy9a5duyRJw4cPL3abESNG6K233rLaonwFBrOoOQDUZMHhxuUrAJQG43gAyHHe2CQFhjoVEJAz46Gg8zuE6squYS6vTX77RLH6HndxpNo2DrK+/zUhS7OWJ1vfJ6UyngMA1HyVer9lcnLOf7RxcXHFbhMbm7ORVEpKSoXEBAAAAMC7qjSOP3LkiJYsWaKHHnpIl19+uerUqSObzWbNACmpzz//XEOHDlWTJk0UEhKiJk2aaOjQofr888+L3UdqaqqmT5+ubt26KS4uTpGRkWrfvr3uvvtu/fnnn8XuZ9u2bbr11lvVqlUrhYWFqW7duurTp49ef/11ORyOYvczf/58XXrppWrYsKFCQ0PVvHlz3XDDDdqwYUOx+wCqssc+SNLkt09o8tsn9MG3qW7lMxaf0uS3T+i+9xLdylZvS7fa/rgv06UsOd1plc3+Jtmt7RtfJSuqfrYiYo1Co40SU9wfaZlOt3ae6nl6ZGW7JiSysl3LDfkKAIAfqNQZFnXr1tWBAwe0Y8cOnXvuucVqs2PHDklSnTp1KjI0v7Vvc6ivQwAAVKDsLCkwJOdrrhmLTynhuEOBATY9cX2MS/3V29K1eHOaJOn6vhE6u3mwVZac7tSjC5IkSR2aBmnshZEubd/4Klm7DuQc6D8jYhQabLPKvt+bqXlrcy5aXtU9XL3ahbi0zb3zsFndQE0YEOVS9v6aFP12yKEHrqlV0rcPoJxUpXF8/fr1y6UfY4xuvfVWzZw50+X1hIQEffzxx/r44491880367XXXpPNZiukF2nv3r0aOHCgfv31V5fXd+7cqZ07d2rWrFl6//33NWDAAK/xvPnmm7r99tuVkZFhvZaenq41a9ZozZo1mjNnjpYsWaLatWsX2kd6erquueYaLVmyxOX1P/74Q3/88Yfef/99PfLII3rwwQe9xgJUdUmpTiWm5Fy9T81wv4p/Ki2nPNDuXpaeZay2WQXygMbIKktJd2+bnO6U3Z5XNzbS/W9DWLD7faExEYX/DckvKMDm9r2ntrXCWesTAFBzVWrCokePHvroo4/07LPP6rrrrlNgoPfDZ2Vl6b///a9sNpt69OhRSVH6l30bw4quBACothwZNgWGGDky8j7sVuSH+NzygqWZjrx+M7Pc2+aWxUW63pV47GS2UtONklLd71YEUHmq6ji+adOmat++vb788ssSt33ggQesZEWnTp00ZcoUtWzZUnv37tXTTz+trVu3aubMmapbt64ee+wxj30kJyfriiuusJIV48eP1/DhwxUWFqYVK1boiSeeUFJSkq655hqtX79eZ511lsd+vvjiC918881yOp2qX7++7r//fnXv3l3Hjx/XG2+8oYULF2rDhg0aOnSoVqxYIbvd88XKm266yUpW9OvXT3fddZcaNWqkn3/+WY8//rj27t2rhx56SA0bNtS4ceNK/DMDqopJV0brSFK2XvsiWeEh7hf0o8LsiolwKjDAvSw0KC8JEFTgT5nNlpdciAh1bxsZapdx5uwNZpzS9NGxxYq3uPUKats4qNRtAQCorio1YXHjjTfqo48+0g8//KCBAwdq9uzZatSokce6CQkJ+sc//qEffvih1NO7AQCAu4r8EJ9bXrA0ODCv3+Ag97a5ZVFhrhfhXv4sWV5ubAZQSarSOP6hhx5S165d1bVrV9WvX1/79u1TixYtStTHnj179PTTT0uSunTpotWrVyssLOdGnq5du+rKK69U3759tXnzZj311FMaO3asWrZs6dbPM888o507d0qSnn76aU2ePNkq69mzp/r166c+ffooNTVVEydO1DfffOPWh8Ph0IQJE+R0OhUdHa1169a5HOuyyy7T7bffrldeeUWrV6/We++9pxtvvNGtn1WrVun999+XJA0aNEgff/yxAgICXN5T586d9eeff2rKlCkaNmyYYmJiSvRzA6qKK/oH6NChAElx+lTSP91q5M3WfPfegmWhfz+keW7t7JLyEgTuc5Ei1aZ/skIinLI7meUAAEBFsBlTuasgDh06VIsWLZLNZlNQUJD69++v7t27q379+rLZbDp06JC+++47ffXVV8rKypIxRkOHDtWHH35YmWHWaPHx8WratKkkqUGD/Tp4sImPIwIAVJSul2ToVIpRdpZNaQdCim5QxfSbcEKh0UaZKTa9PZk7DFGz5R+j7d+/X02aVK0xWlUdx+dPWIwePVpz5swpsk1uAkCS1q9f73EWyIYNG9SzZ09J0oQJE/Tiiy+6lGdlZalevXpKTExU+/bt9csvv3ic+XDrrbfq9ddflyRt3rxZnTt3din/4IMPdO2110qSnnjiCd17r9vVVaWmpqpJkyY6ceKEzjjjDP38889udQYOHKhly5YpICBA+/bt8/j7M3/+fI0YMUJSTrJl0qRJ7j+cMqjqv8OoOZo0kRISfBtD48ZSfLxvYwAAoDiq2xitUmdYSNK8efN044036oMPPlBmZqaWLVumZcuWudXLzaNcc801eueddyo7TL8R0ziz6EoAgGrr4PYQn3+gL4tFz0TLbpfq1ZU0ucjqACpQTRnHG2P0ySefSJLatWtX6JJVPXr0UNu2bfXrr79q0aJFeuGFF1z2sli5cqUSExMl5SRKClumacyYMVbCYuHChW4Ji0WLFrnU9SQ8PFzXXnutXn/9df3yyy/avXu3WrdubZUnJyfr66+/liT179+/0A+hQ4cOVXR0tE6ePKmFCxeWe8ICqGx2u9SwoW+O3aCBb44LAEBNV+kJi5CQEC1YsEA33nijXnnlFa1atUqpqakudcLDw9W3b1/dfvvtRW5Oh7I5Z3CKr0MAAFSg6v5h+uDBADmdUlRw0XUBVKyaMo7//ffflfB3Jrdv375e6/bt21e//vqr4uPj3ZaeWrNmjUu9wnTp0kURERFKSUnR2rVr3cpz+2nbtq0aePmj3bdvXyvxsXbtWpeExcaNG63Nur3FEhwcrB49eujLL7/Uxo0blZWVpaCgoELrA1VVTOMsOcOluFjpl+/4HQYAoCap9IRFroEDB2rgwIHKzs7Wb7/9puPHj0uS4uLidPrpp1vrrQIAgNLbvFk6dCJb//30ZLHq3z+slmIi8u4SXr0tXYs3pxXZrn5MgO4eHO3y2htfJWvXgawi257fIVRXdg1zeW3y2yckSYcOStnZkiPdLqlWMd4BgIpW3cfxO3bssJ63a9fOa9385Tt27HBJWBS3n8DAQLVs2VI//fSTSxspZ2ZE/N9rypQ0lvxK+p6+/PJLORwO7d69Wx06dPBaHyjKfe8lypFt1DguUBMHRbmUffBtqjbuzkmmTboyWg1i8/4+7D2Upde+SJYk9TsjVAM6u44FHvsgSUmpTtUKt+uBa1zHAD1Hn5IkpZ+0Kf+eEwAAoPrzWcIiV0BAgMvdQahc2dm+jgAAUNGyjVFiSvG2rHIWqJaeVby2YcHudZLTncVqm5bpdHstt13o3zmQ9JPudQD4VnUdx+/fv996XtT6vblr/RZsl//7iIiIIjevbtq0qX766ScdPXpUGRkZCgnJ2VMoPj7eWkKrPGIpTT8lSVjEF7Fg/8GDB4vdF2qOxGSnHE4pOsz9/+rUjLxxRHaB7TMd2Xn/36dnuo8XklJzxxHu/Wam2RQcZhQSaTT57RMa1jNc3dvk7dVVkTdrJKYYBQdKLeoHut2sAQAAyq5SExanTp3Sc889J0m6+eabvU55lnIGvG+88YYkafLkyQoLC/NaHyXnyLAVXQkAUK0F2GyKiSje33t7gWqhQcVrGx3uXicy1F6stmHB7uu+57Y7ccrIZpfsVfuGbaDGq0nj+FOnTlnPIyMjvdaNiIiwnicnJ3vsp6g+PPWTm7Ao71jK2k9R8ic7gFwxkXY5so2iwtz/Pw8PyRtHBNhcxwSBAXn/34cGu48XaoXbJTn//urKZstJcNjsOQmETIdrwqOib9bIdEiHE7n7DwCAilCpCYtFixbpkUceUevWrfXQQw8VWb9BgwaaO3eu9uzZo3bt2unaa6+thCgBAKhZGsQGaPro0i2X0KdjqPp0DC1V2/H9i76IV5jceMe9lLPUTFBY8S46AKgYNWkcn56ebj0PDva+QU5uYkGS0tJc77jO7aeoPrz1U96xlLUfoCS6dJEOHZKkGOu1Z24rWCv874c07/6CZUHKXc5pnscj5C0D9do/XUv63GZTUKiRcUqxUTYFB7omPCrjZg1PiRQAAFB2lZqwWLhwoWw2W7E/sNhsNg0fPlz/+c9/9MEHH1SpDzoAAACAv6hJ4/jQ0LwkbGZmpte6uRtZS3KbJZLbT1F9eOunvGMpaz9FKbgUVUEHDx5Ut27dStQnqq9Dh6S/96/3mYxkm6ZPcL8pw1c3awAAgLKr1ITFzp07JUnnnXdesdv07NlTkrR9+/YKiQkAAFRdjkwpKDTnKwDfqUnj+KiovE2Bi1oSKSUlxXpecKml3H6Ks6xSYf2Udyxl7acoRe2PAf/S9Jx0xbY1ys606eTvlXuBP+DvpSJZMhIAgJqnUhMWuZu0NWzYsNhtctfHTfD1rRs1VFAoS2wAAKqu7MycJR+yM9lzCfClmjSOz3/RvahNpPPPKCi4f0OTJk303XffKSUlRYmJiV433s7tp27dui5LMpVnLPn76dKlS6n6QfX02AdJSkrN25i64MyCdTsztOi7VEnSiN4ROrdl3rJh6ZlGD85LlCS1aRTktpzj7G+StX1/liTp4etqKTI0bxmkMwbk9Jl+0qZ3763chMXkt6XEFKlevUo9LAAAqASVuuii3Z5zuNTU1GK3ya3rcDgqJCZ/Z2fZTQAAABShJo3jO3ToYD3PnTlSmPzl7du3L1U/DodDe/fu9dhHZGSklTSojFjylwcGBqpVq1Ze66J6iImwKy7SrkyHPG4Wnfn3JtKeNqc2klWWnO50a5uSntfWFOh659clW1IMAACgOCp1hkXDhg21e/dubd68udjTyTdv3iwp7w4tAADgP378NFLHE43iYmzSvb6OBvBfNWkc36JFCzVq1EgHDhzQqlWrvNZdvXq1JKlx48Zq3ry5S1nv3r2t56tWrVKPHj089rF582ZrGaZevXq5lffu3Vvz5s3Tr7/+qkOHDhX688ofa8F+unbtquDgYGVmZmrVqlW6917PfzAzMzO1YcMGlzao/iYMiNKxk9l6+bNkBQe6Jx2C820iXXBzaptkleWfPZErIjSvra3AZMfkvwKUcsImGZsmv32iyDhDg2z6z8gYl9c++DZVG3dneG6Qz1nNgnXDBRHW95GhdiWmZBfZDgAAVD+VmrA4//zztWvXLr3yyiu67bbbFBQU5LV+VlaWXnnlFdlsNpcPBCg/62bXkh70dRQAAHh2/M+gnA09G/s6EsC/1aRxvM1m0+DBg/Xqq69q586d2rBhg8dkw4YNG6zZCIMHD5atwNXaCy64QLVq1VJSUpLefvttTZkyxa2OJM2ZM8d6PmTIELfyq666SvPmzbPqeko2pKam6n//+5+knNkUbdq0cSmPiorSRRddpM8++0zLly9XfHy8x/0mFi5cqJMnTxYaC6qnLl2kQ4cCJNWSJM27v2CNkL8f0jy31jZJeUtIPepWnrdEVMF+Dx4M1g+rgtX5okxFDC16L5fQIPfZH6kZxuOskIJSMlwTMbdfHqmnPj5ZZDsAAFD9VOqCQGPHjpUk7d69WyNHjvQ6pTw1NVUjRozQrl27XNqifGWlsSYUAKDq6jUuSaOePKFe45J8HQrg12raOH7ixIkKDMy5d+uOO+5QWlqaS3laWpruuOMOSTlLJ02cONGtj+DgYN15552SpB07duiZZ55xq7N+/Xq9+eabkqS+ffuqa9eubnWGDBmili1bSpKeeOIJa/mo/CZPnqwTJ05Yzz25++67JeUsQXX77bcrO9v17vNjx47pnnvukSTFxMRo3LhxHvtB9XPokJSQUPkP5985hGxHziyNoh/unz3DQ4rTzqaIENe2daIDVCvcrlrhfJ4FAKCmsRlTcCXKijVy5EjNnz9fNptNjRs31vjx49WnTx81bNhQNptNBw4c0OrVqzVr1ixr47lhw4ZpwYIFlRlmjRYfH2+tldugwX4dPOh+9xUAAFXBDU+eUGi0+XtDz9iiGwDVWP4x2v79+z3eIe9LVWUcv3btWu3Zs8f6/tixY9ZF/F69erldiB8zZozHfu677z49+eSTkqROnTrpnnvuUcuWLbV371499dRT2rp1q1Xv8ccf99jHqVOn1KVLFys5c/PNN2v48OEKCwvTihUr9Pjjjys5OVlhYWH69ttvdc4553jsZ9myZRo0aJCcTqfq16+vBx54QN26ddOJEyf0xhtv6KOPPpKUs3zUypUrFRAQ4LGfESNGaP78+ZKkfv36aeLEiWrUqJF+/vlnTZs2zUqGvPbaa7rllls89lEWVf13uCLl3/j6riui1KR23mIGfxxx6KXPTkmSzu8Qqiu7uu798NTCkzp2KlsRIXY9MryWS9mXP6Tpqx/TJUnjLo5U28Z5s5uOnszW0x+f1J+/2bXspUilJgaoYcMKeXteNWgg/b0CHAAAqIKq2xit0hMW6enpuvLKK7V8+XKPU6Zz5YbVv39/ffLJJwoNDa2sEGs8EhYAgOriukcSFRBslJ1p04JHYnwdDlChqvoHiaoyjh8zZozefvvtYtcv7OOO0+nU+PHj9dZbbxXa9qabbtLMmTOtTcc92bNnjwYMGKDdu3d7LI+OjtbcuXN1xRVXeI3zjTfe0IQJE5SZmemxvFu3blq6dKnq1KlTaB9paWkaNmyYli1b5rHcbrfrwQcf1COPPOI1ltKq6r/DFWny2yespY0euCZazermJSz2HsrSkwt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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_MC = hist_MC_conv_nominal\n",
    "\n",
    "filepath = '../systematics/splines/DOMEffSplines/STERILE_domefficiency_spline_stacked_atmConv_track.fits'\n",
    "\n",
    "domeffVariation_p = get_detector(combined_MC_chargeSorted[:,1],combined_MC_chargeSorted[:,2],1.27,1.29,filepath)\n",
    "domeffVariation_m = get_detector(combined_MC_chargeSorted[:,1],combined_MC_chargeSorted[:,2],1.27,1.25,filepath)\n",
    "\n",
    "totalWeights_conv_p = domeffVariation_p * totalWeights_conv_\n",
    "totalWeights_conv_m = domeffVariation_m * totalWeights_conv_\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(8, 3), dpi=200)\n",
    "\n",
    "x_data = combined_MC_chargeSorted[:,2] # cosZenith\n",
    "y_data = combined_MC_chargeSorted[:,1] # energy\n",
    "x_bin_edges = zenith_binedges\n",
    "y_bin_edges = energy_binedges\n",
    "hist_domeffVariation_p, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_conv_p, bins=[x_bin_edges, y_bin_edges])\n",
    "hist_domeffVariation_m, x_edges, y_edges = np.histogram2d(x_data, y_data, weights=totalWeights_conv_m, bins=[x_bin_edges, y_bin_edges])\n",
    "\n",
    "ax1.stairs(np.sum(hist_MC,0), energy_binedges, edgecolor='blue')\n",
    "ax1.stairs(np.sum(hist_domeffVariation_p,0), energy_binedges, edgecolor='cornflowerblue', linestyle=':')\n",
    "ax1.stairs(np.sum(hist_domeffVariation_m,0), energy_binedges, edgecolor='cornflowerblue',linestyle='--')\n",
    "ax1.set_xlabel('muon energy / GeV'); ax1.set_xscale('log'); ax1.set_xlim(energy_binedges[0], energy_binedges[-1])\n",
    "ax1.set_ylabel('counts'); ax1.set_yscale('log')\n",
    "\n",
    "# Plot the histogram\n",
    "ax2.stairs(np.sum(hist_MC,1), zenith_binedges, edgecolor='blue', label='MC (unfitted)')\n",
    "ax2.stairs(np.sum(hist_domeffVariation_p,1), zenith_binedges, edgecolor='cornflowerblue', label=r'domeff: 1.29', linestyle=':')\n",
    "ax2.stairs(np.sum(hist_domeffVariation_m,1), zenith_binedges, edgecolor='cornflowerblue', label=r'domeff: 1.25', linestyle='--')\n",
    "ax2.set_xlabel('cos(Zenith)'); ax2.set_xlim(zenith_binedges[0], zenith_binedges[-1])\n",
    "ax2.set_ylabel('counts');\n",
    "\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('systematics_example2.png', bbox_inches='tight',dpi=400)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-data_env2]",
   "language": "python",
   "name": "conda-env-.conda-data_env2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
